Linear dynamical modes as new variables for data-driven ENSO forecast

NASA Astrophysics Data System (ADS)

Gavrilov, Andrey; Seleznev, Aleksei; Mukhin, Dmitry; Loskutov, Evgeny; Feigin, Alexander; Kurths, Juergen

2018-05-01

A new data-driven model for analysis and prediction of spatially distributed time series is proposed. The model is based on a linear dynamical mode (LDM) decomposition of the observed data which is derived from a recently developed nonlinear dimensionality reduction approach. The key point of this approach is its ability to take into account simple dynamical properties of the observed system by means of revealing the system's dominant time scales. The LDMs are used as new variables for empirical construction of a nonlinear stochastic evolution operator. The method is applied to the sea surface temperature anomaly field in the tropical belt where the El Nino Southern Oscillation (ENSO) is the main mode of variability. The advantage of LDMs versus traditionally used empirical orthogonal function decomposition is demonstrated for this data. Specifically, it is shown that the new model has a competitive ENSO forecast skill in comparison with the other existing ENSO models.

Dynamic correlations at different time-scales with empirical mode decomposition

NASA Astrophysics Data System (ADS)

Nava, Noemi; Di Matteo, T.; Aste, Tomaso

2018-07-01

We introduce a simple approach which combines Empirical Mode Decomposition (EMD) and Pearson's cross-correlations over rolling windows to quantify dynamic dependency at different time scales. The EMD is a tool to separate time series into implicit components which oscillate at different time-scales. We apply this decomposition to intraday time series of the following three financial indices: the S&P 500 (USA), the IPC (Mexico) and the VIX (volatility index USA), obtaining time-varying multidimensional cross-correlations at different time-scales. The correlations computed over a rolling window are compared across the three indices, across the components at different time-scales and across different time lags. We uncover a rich heterogeneity of interactions, which depends on the time-scale and has important lead-lag relations that could have practical use for portfolio management, risk estimation and investment decisions.

Subgrid-scale physical parameterization in atmospheric modeling: How can we make it consistent?

NASA Astrophysics Data System (ADS)

Yano, Jun-Ichi

2016-07-01

Approaches to subgrid-scale physical parameterization in atmospheric modeling are reviewed by taking turbulent combustion flow research as a point of reference. Three major general approaches are considered for its consistent development: moment, distribution density function (DDF), and mode decomposition. The moment expansion is a standard method for describing the subgrid-scale turbulent flows both in geophysics and engineering. The DDF (commonly called PDF) approach is intuitively appealing as it deals with a distribution of variables in subgrid scale in a more direct manner. Mode decomposition was originally applied by Aubry et al (1988 J. Fluid Mech. 192 115-73) in the context of wall boundary-layer turbulence. It is specifically designed to represent coherencies in compact manner by a low-dimensional dynamical system. Their original proposal adopts the proper orthogonal decomposition (empirical orthogonal functions) as their mode-decomposition basis. However, the methodology can easily be generalized into any decomposition basis. Among those, wavelet is a particularly attractive alternative. The mass-flux formulation that is currently adopted in the majority of atmospheric models for parameterizing convection can also be considered a special case of mode decomposition, adopting segmentally constant modes for the expansion basis. This perspective further identifies a very basic but also general geometrical constraint imposed on the massflux formulation: the segmentally-constant approximation. Mode decomposition can, furthermore, be understood by analogy with a Galerkin method in numerically modeling. This analogy suggests that the subgrid parameterization may be re-interpreted as a type of mesh-refinement in numerical modeling. A link between the subgrid parameterization and downscaling problems is also pointed out.

Time-frequency analysis of neuronal populations with instantaneous resolution based on noise-assisted multivariate empirical mode decomposition.

PubMed

Alegre-Cortés, J; Soto-Sánchez, C; Pizá, Á G; Albarracín, A L; Farfán, F D; Felice, C J; Fernández, E

2016-07-15

Linear analysis has classically provided powerful tools for understanding the behavior of neural populations, but the neuron responses to real-world stimulation are nonlinear under some conditions, and many neuronal components demonstrate strong nonlinear behavior. In spite of this, temporal and frequency dynamics of neural populations to sensory stimulation have been usually analyzed with linear approaches. In this paper, we propose the use of Noise-Assisted Multivariate Empirical Mode Decomposition (NA-MEMD), a data-driven template-free algorithm, plus the Hilbert transform as a suitable tool for analyzing population oscillatory dynamics in a multi-dimensional space with instantaneous frequency (IF) resolution. The proposed approach was able to extract oscillatory information of neurophysiological data of deep vibrissal nerve and visual cortex multiunit recordings that were not evidenced using linear approaches with fixed bases such as the Fourier analysis. Texture discrimination analysis performance was increased when Noise-Assisted Multivariate Empirical Mode plus Hilbert transform was implemented, compared to linear techniques. Cortical oscillatory population activity was analyzed with precise time-frequency resolution. Similarly, NA-MEMD provided increased time-frequency resolution of cortical oscillatory population activity. Noise-Assisted Multivariate Empirical Mode Decomposition plus Hilbert transform is an improved method to analyze neuronal population oscillatory dynamics overcoming linear and stationary assumptions of classical methods. Copyright © 2016 Elsevier B.V. All rights reserved.

Experimental Modal Analysis and Dynamic Component Synthesis. Volume 3. Modal Parameter Estimation

DTIC Science & Technology

1987-12-01

residues as well as poles is achieved. A singular value decomposition method has been used to develop a complex mode indicator function ( CMIF )[70...which can be used to help determine the number of poles before the analysis. The CMIF is formed by performing a singular value decomposition of all of...servo systems which can include both low and high damping modes. "• CMIF can be used to indicate close or repeated eigenvalues before the parameter

Koopman Mode Decomposition Methods in Dynamic Stall: Reduced Order Modeling and Control

DTIC Science & Technology

2015-11-10

the flow phenomena by separating them into individual modes. The technique of Proper Orthogonal Decomposition (POD), see [ Holmes : 1998] is a popular...sampled values h(k), k = 0,…,2M-1, of the exponential sum 1. Solve the following linear system where 2. Compute all zeros zj  D, j = 1,…,M...of the Prony polynomial i.e., calculate all eigenvalues of the associated companion matrix and form fj = log zj for j = 1,…,M, where log is the

Analysis of Self-Excited Combustion Instabilities Using Decomposition Techniques

DTIC Science & Technology

2016-07-05

are evaluated for the study of self-excited longitudinal combustion instabilities in laboratory-scaled single-element gas turbine and rocket...Air Force Base, California 93524 DOI: 10.2514/1.J054557 Proper orthogonal decomposition and dynamic mode decomposition are evaluated for the study of...instabilities. In addition, we also evaluate the capabilities of the methods to deal with data sets of different spatial extents and temporal resolution

Reduced quantum dynamics with arbitrary bath spectral densities: hierarchical equations of motion based on several different bath decomposition schemes.

PubMed

Liu, Hao; Zhu, Lili; Bai, Shuming; Shi, Qiang

2014-04-07

We investigated applications of the hierarchical equation of motion (HEOM) method to perform high order perturbation calculations of reduced quantum dynamics for a harmonic bath with arbitrary spectral densities. Three different schemes are used to decompose the bath spectral density into analytical forms that are suitable to the HEOM treatment: (1) The multiple Lorentzian mode model that can be obtained by numerically fitting the model spectral density. (2) The combined Debye and oscillatory Debye modes model that can be constructed by fitting the corresponding classical bath correlation function. (3) A new method that uses undamped harmonic oscillator modes explicitly in the HEOM formalism. Methods to extract system-bath correlations were investigated for the above bath decomposition schemes. We also show that HEOM in the undamped harmonic oscillator modes can give detailed information on the partial Wigner transform of the total density operator. Theoretical analysis and numerical simulations of the spin-Boson dynamics and the absorption line shape of molecular dimers show that the HEOM formalism for high order perturbations can serve as an important tool in studying the quantum dissipative dynamics in the intermediate coupling regime.

Reduced quantum dynamics with arbitrary bath spectral densities: Hierarchical equations of motion based on several different bath decomposition schemes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Hao; Zhu, Lili; Bai, Shuming

2014-04-07

We investigated applications of the hierarchical equation of motion (HEOM) method to perform high order perturbation calculations of reduced quantum dynamics for a harmonic bath with arbitrary spectral densities. Three different schemes are used to decompose the bath spectral density into analytical forms that are suitable to the HEOM treatment: (1) The multiple Lorentzian mode model that can be obtained by numerically fitting the model spectral density. (2) The combined Debye and oscillatory Debye modes model that can be constructed by fitting the corresponding classical bath correlation function. (3) A new method that uses undamped harmonic oscillator modes explicitly inmore » the HEOM formalism. Methods to extract system-bath correlations were investigated for the above bath decomposition schemes. We also show that HEOM in the undamped harmonic oscillator modes can give detailed information on the partial Wigner transform of the total density operator. Theoretical analysis and numerical simulations of the spin-Boson dynamics and the absorption line shape of molecular dimers show that the HEOM formalism for high order perturbations can serve as an important tool in studying the quantum dissipative dynamics in the intermediate coupling regime.« less

Robust-mode analysis of hydrodynamic flows

NASA Astrophysics Data System (ADS)

Roy, Sukesh; Gord, James R.; Hua, Jia-Chen; Gunaratne, Gemunu H.

2017-04-01

The emergence of techniques to extract high-frequency high-resolution data introduces a new avenue for modal decomposition to assess the underlying dynamics, especially of complex flows. However, this task requires the differentiation of robust, repeatable flow constituents from noise and other irregular features of a flow. Traditional approaches involving low-pass filtering and principle components analysis have shortcomings. The approach outlined here, referred to as robust-mode analysis, is based on Koopman decomposition. Three applications to (a) a counter-rotating cellular flame state, (b) variations in financial markets, and (c) turbulent injector flows are provided.

Defect inspection using a time-domain mode decomposition technique

NASA Astrophysics Data System (ADS)

Zhu, Jinlong; Goddard, Lynford L.

2018-03-01

In this paper, we propose a technique called time-varying frequency scanning (TVFS) to meet the challenges in killer defect inspection. The proposed technique enables the dynamic monitoring of defects by checking the hopping in the instantaneous frequency data and the classification of defect types by comparing the difference in frequencies. The TVFS technique utilizes the bidimensional empirical mode decomposition (BEMD) method to separate the defect information from the sea of system errors. This significantly improve the signal-to-noise ratio (SNR) and moreover, it potentially enables reference-free defect inspection.

Structural system identification based on variational mode decomposition

NASA Astrophysics Data System (ADS)

Bagheri, Abdollah; Ozbulut, Osman E.; Harris, Devin K.

2018-03-01

In this paper, a new structural identification method is proposed to identify the modal properties of engineering structures based on dynamic response decomposition using the variational mode decomposition (VMD). The VMD approach is a decomposition algorithm that has been developed as a means to overcome some of the drawbacks and limitations of the empirical mode decomposition method. The VMD-based modal identification algorithm decomposes the acceleration signal into a series of distinct modal responses and their respective center frequencies, such that when combined their cumulative modal responses reproduce the original acceleration response. The decaying amplitude of the extracted modal responses is then used to identify the modal damping ratios using a linear fitting function on modal response data. Finally, after extracting modal responses from available sensors, the mode shape vector for each of the decomposed modes in the system is identified from all obtained modal response data. To demonstrate the efficiency of the algorithm, a series of numerical, laboratory, and field case studies were evaluated. The laboratory case study utilized the vibration response of a three-story shear frame, whereas the field study leveraged the ambient vibration response of a pedestrian bridge to characterize the modal properties of the structure. The modal properties of the shear frame were computed using analytical approach for a comparison with the experimental modal frequencies. Results from these case studies demonstrated that the proposed method is efficient and accurate in identifying modal data of the structures.

Dynamic characterization of a damaged beam using empirical mode decomposition and Hilbert spectrum method

NASA Astrophysics Data System (ADS)

Chang, Chih-Chen; Poon, Chun-Wing

2004-07-01

Recently, the empirical mode decomposition (EMD) in combination with the Hilbert spectrum method has been proposed to identify the dynamic characteristics of linear structures. In this study, this EMD and Hilbert spectrum method is used to analyze the dynamic characteristics of a damaged reinforced concrete (RC) beam in the laboratory. The RC beam is 4m long with a cross section of 200mm X 250mm. The beam is sequentially subjected to a concentrated load of different magnitudes at the mid-span to produce different degrees of damage. An impact load is applied around the mid-span to excite the beam. Responses of the beam are recorded by four accelerometers. Results indicate that the EMD and Hilbert spectrum method can reveal the variation of the dynamic characteristics in the time domain. These results are also compared with those obtained using the Fourier analysis. In general, it is found that the two sets of results correlate quite well in terms of mode counts and frequency values. Some differences, however, can be seen in the damping values, which perhaps can be attributed to the linear assumption of the Fourier transform.

Dynamic mode decomposition of Fontan hemodynamics in an idealized total cavopulmonary connection

NASA Astrophysics Data System (ADS)

Delorme, Yann T.; Kerlo, Anna-Elodie M.; Anupindi, Kameswararao; Rodefeld, Mark D.; Frankel, Steven H.

2014-08-01

Univentricular heart disease is the leading cause of death from any birth defect in the first year of life. Typically, patients have to undergo three open heart surgical procedures within the first few years of their lives to eventually directly connect the superior and inferior vena cavae to the left and right pulmonary arteries forming the total cavopulmonary connection (TCPC). The end result is a weak circulation where the single working ventricle pumps oxygenated blood to the body and de-oxygenated blood flows passively through the TCPC into the lungs. The fluid dynamics of the TCPC junction involve confined impinging jets resulting in a highly unstable flow, significant mechanical energy dissipation and undesirable pressure loss. Understanding and predicting such flows is important for improving the surgical procedure and for the design of mechanical cavopulmonary assist devices. In this study, dynamic mode decomposition (DMD) is used to analyze previously obtained stereoscopic particle imaging velocimetry (SPIV) data and large eddy simulation (LES) results for an idealized TCPC. Analysis of the DMD modes from the SPIV and LES serves to both highlight the unsteady vortical dynamics and the qualitative agreement between measurements and simulations.

Numerical Schemes and Computational Studies for Dynamically Orthogonal Equations (Multidisciplinary Simulation, Estimation, and Assimilation Systems: Reports in Ocean Science and Engineering)

DTIC Science & Technology

2011-08-01

heat transfers [49, 52]. However, the DO method has not yet been applied to Boussinesq flows, and the numerical challenges of the DO decomposition for...used a PCE scheme to study mixing in a two-dimensional (2D) microchannel and improved the efficiency of their solution scheme by decoupling the...to several Navier-Stokes flows and their stochastic dynamics has been studied, including mean-mode and mode-mode energy transfers for 2D flows and

Towards reduced order modelling for predicting the dynamics of coherent vorticity structures within wind turbine wakes

NASA Astrophysics Data System (ADS)

Debnath, M.; Santoni, C.; Leonardi, S.; Iungo, G. V.

2017-03-01

The dynamics of the velocity field resulting from the interaction between the atmospheric boundary layer and a wind turbine array can affect significantly the performance of a wind power plant and the durability of wind turbines. In this work, dynamics in wind turbine wakes and instabilities of helicoidal tip vortices are detected and characterized through modal decomposition techniques. The dataset under examination consists of snapshots of the velocity field obtained from large-eddy simulations (LES) of an isolated wind turbine, for which aerodynamic forcing exerted by the turbine blades on the atmospheric boundary layer is mimicked through the actuator line model. Particular attention is paid to the interaction between the downstream evolution of the helicoidal tip vortices and the alternate vortex shedding from the turbine tower. The LES dataset is interrogated through different modal decomposition techniques, such as proper orthogonal decomposition and dynamic mode decomposition. The dominant wake dynamics are selected for the formulation of a reduced order model, which consists in a linear time-marching algorithm where temporal evolution of flow dynamics is obtained from the previous temporal realization multiplied by a time-invariant operator. This article is part of the themed issue 'Wind energy in complex terrains'.

Extracting Leading Nonlinear Modes of Changing Climate From Global SST Time Series

NASA Astrophysics Data System (ADS)

Mukhin, D.; Gavrilov, A.; Loskutov, E. M.; Feigin, A. M.; Kurths, J.

2017-12-01

Data-driven modeling of climate requires adequate principal variables extracted from observed high-dimensional data. For constructing such variables it is needed to find spatial-temporal patterns explaining a substantial part of the variability and comprising all dynamically related time series from the data. The difficulties of this task rise from the nonlinearity and non-stationarity of the climate dynamical system. The nonlinearity leads to insufficiency of linear methods of data decomposition for separating different processes entangled in the observed time series. On the other hand, various forcings, both anthropogenic and natural, make the dynamics non-stationary, and we should be able to describe the response of the system to such forcings in order to separate the modes explaining the internal variability. The method we present is aimed to overcome both these problems. The method is based on the Nonlinear Dynamical Mode (NDM) decomposition [1,2], but takes into account external forcing signals. An each mode depends on hidden, unknown a priori, time series which, together with external forcing time series, are mapped onto data space. Finding both the hidden signals and the mapping allows us to study the evolution of the modes' structure in changing external conditions and to compare the roles of the internal variability and forcing in the observed behavior. The method is used for extracting of the principal modes of SST variability on inter-annual and multidecadal time scales accounting the external forcings such as CO2, variations of the solar activity and volcanic activity. The structure of the revealed teleconnection patterns as well as their forecast under different CO2 emission scenarios are discussed.[1] Mukhin, D., Gavrilov, A., Feigin, A., Loskutov, E., & Kurths, J. (2015). Principal nonlinear dynamical modes of climate variability. Scientific Reports, 5, 15510. [2] Gavrilov, A., Mukhin, D., Loskutov, E., Volodin, E., Feigin, A., & Kurths, J. (2016). Method for reconstructing nonlinear modes with adaptive structure from multidimensional data. Chaos: An Interdisciplinary Journal of Nonlinear Science, 26(12), 123101.

Towards reduced order modelling for predicting the dynamics of coherent vorticity structures within wind turbine wakes.

PubMed

Debnath, M; Santoni, C; Leonardi, S; Iungo, G V

2017-04-13

The dynamics of the velocity field resulting from the interaction between the atmospheric boundary layer and a wind turbine array can affect significantly the performance of a wind power plant and the durability of wind turbines. In this work, dynamics in wind turbine wakes and instabilities of helicoidal tip vortices are detected and characterized through modal decomposition techniques. The dataset under examination consists of snapshots of the velocity field obtained from large-eddy simulations (LES) of an isolated wind turbine, for which aerodynamic forcing exerted by the turbine blades on the atmospheric boundary layer is mimicked through the actuator line model. Particular attention is paid to the interaction between the downstream evolution of the helicoidal tip vortices and the alternate vortex shedding from the turbine tower. The LES dataset is interrogated through different modal decomposition techniques, such as proper orthogonal decomposition and dynamic mode decomposition. The dominant wake dynamics are selected for the formulation of a reduced order model, which consists in a linear time-marching algorithm where temporal evolution of flow dynamics is obtained from the previous temporal realization multiplied by a time-invariant operator.This article is part of the themed issue 'Wind energy in complex terrains'. © 2017 The Author(s).

Fault feature analysis of cracked gear based on LOD and analytical-FE method

NASA Astrophysics Data System (ADS)

Wu, Jiateng; Yang, Yu; Yang, Xingkai; Cheng, Junsheng

2018-01-01

At present, there are two main ideas for gear fault diagnosis. One is the model-based gear dynamic analysis; the other is signal-based gear vibration diagnosis. In this paper, a method for fault feature analysis of gear crack is presented, which combines the advantages of dynamic modeling and signal processing. Firstly, a new time-frequency analysis method called local oscillatory-characteristic decomposition (LOD) is proposed, which has the attractive feature of extracting fault characteristic efficiently and accurately. Secondly, an analytical-finite element (analytical-FE) method which is called assist-stress intensity factor (assist-SIF) gear contact model, is put forward to calculate the time-varying mesh stiffness (TVMS) under different crack states. Based on the dynamic model of the gear system with 6 degrees of freedom, the dynamic simulation response was obtained for different tooth crack depths. For the dynamic model, the corresponding relation between the characteristic parameters and the degree of the tooth crack is established under a specific condition. On the basis of the methods mentioned above, a novel gear tooth root crack diagnosis method which combines the LOD with the analytical-FE is proposed. Furthermore, empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) are contrasted with the LOD by gear crack fault vibration signals. The analysis results indicate that the proposed method performs effectively and feasibility for the tooth crack stiffness calculation and the gear tooth crack fault diagnosis.

Wavelet-bounded empirical mode decomposition for measured time series analysis

NASA Astrophysics Data System (ADS)

Moore, Keegan J.; Kurt, Mehmet; Eriten, Melih; McFarland, D. Michael; Bergman, Lawrence A.; Vakakis, Alexander F.

2018-01-01

Empirical mode decomposition (EMD) is a powerful technique for separating the transient responses of nonlinear and nonstationary systems into finite sets of nearly orthogonal components, called intrinsic mode functions (IMFs), which represent the dynamics on different characteristic time scales. However, a deficiency of EMD is the mixing of two or more components in a single IMF, which can drastically affect the physical meaning of the empirical decomposition results. In this paper, we present a new approached based on EMD, designated as wavelet-bounded empirical mode decomposition (WBEMD), which is a closed-loop, optimization-based solution to the problem of mode mixing. The optimization routine relies on maximizing the isolation of an IMF around a characteristic frequency. This isolation is measured by fitting a bounding function around the IMF in the frequency domain and computing the area under this function. It follows that a large (small) area corresponds to a poorly (well) separated IMF. An optimization routine is developed based on this result with the objective of minimizing the bounding-function area and with the masking signal parameters serving as free parameters, such that a well-separated IMF is extracted. As examples of application of WBEMD we apply the proposed method, first to a stationary, two-component signal, and then to the numerically simulated response of a cantilever beam with an essentially nonlinear end attachment. We find that WBEMD vastly improves upon EMD and that the extracted sets of IMFs provide insight into the underlying physics of the response of each system.

Reconstructing multi-mode networks from multivariate time series

NASA Astrophysics Data System (ADS)

Gao, Zhong-Ke; Yang, Yu-Xuan; Dang, Wei-Dong; Cai, Qing; Wang, Zhen; Marwan, Norbert; Boccaletti, Stefano; Kurths, Jürgen

2017-09-01

Unveiling the dynamics hidden in multivariate time series is a task of the utmost importance in a broad variety of areas in physics. We here propose a method that leads to the construction of a novel functional network, a multi-mode weighted graph combined with an empirical mode decomposition, and to the realization of multi-information fusion of multivariate time series. The method is illustrated in a couple of successful applications (a multi-phase flow and an epileptic electro-encephalogram), which demonstrate its powerfulness in revealing the dynamical behaviors underlying the transitions of different flow patterns, and enabling to differentiate brain states of seizure and non-seizure.

Koopman decomposition of Burgers' equation: What can we learn?

NASA Astrophysics Data System (ADS)

Page, Jacob; Kerswell, Rich

2017-11-01

Burgers' equation is a well known 1D model of the Navier-Stokes equations and admits a selection of equilibria and travelling wave solutions. A series of Burgers' trajectories are examined with Dynamic Mode Decomposition (DMD) to probe the capability of the method to extract coherent structures from ``run-down'' simulations. The performance of the method depends critically on the choice of observable. We use the Cole-Hopf transformation to derive an observable which has linear, autonomous dynamics and for which the DMD modes overlap exactly with Koopman modes. This observable can accurately predict the flow evolution beyond the time window of the data used in the DMD, and in that sense outperforms other observables motivated by the nonlinearity in the governing equation. The linearizing observable also allows us to make informed decisions about often ambiguous choices in nonlinear problems, such as rank truncation and snapshot spacing. A number of rules of thumb for connecting DMD with the Koopman operator for nonlinear PDEs are distilled from the results. Related problems in low Reynolds number fluid turbulence are also discussed.

Spectral decomposition of nonlinear systems with memory

NASA Astrophysics Data System (ADS)

Svenkeson, Adam; Glaz, Bryan; Stanton, Samuel; West, Bruce J.

2016-02-01

We present an alternative approach to the analysis of nonlinear systems with long-term memory that is based on the Koopman operator and a Lévy transformation in time. Memory effects are considered to be the result of interactions between a system and its surrounding environment. The analysis leads to the decomposition of a nonlinear system with memory into modes whose temporal behavior is anomalous and lacks a characteristic scale. On average, the time evolution of a mode follows a Mittag-Leffler function, and the system can be described using the fractional calculus. The general theory is demonstrated on the fractional linear harmonic oscillator and the fractional nonlinear logistic equation. When analyzing data from an ill-defined (black-box) system, the spectral decomposition in terms of Mittag-Leffler functions that we propose may uncover inherent memory effects through identification of a small set of dynamically relevant structures that would otherwise be obscured by conventional spectral methods. Consequently, the theoretical concepts we present may be useful for developing more general methods for numerical modeling that are able to determine whether observables of a dynamical system are better represented by memoryless operators, or operators with long-term memory in time, when model details are unknown.

Modal decomposition of turbulent supersonic cavity

NASA Astrophysics Data System (ADS)

Soni, R. K.; Arya, N.; De, A.

2018-06-01

Self-sustained oscillations in a Mach 3 supersonic cavity with a length-to-depth ratio of three are investigated using wall-modeled large eddy simulation methodology for ReD = 3.39× 105 . The unsteady data obtained through computation are utilized to investigate the spatial and temporal evolution of the flow field, especially the second invariant of the velocity tensor, while the phase-averaged data are analyzed over a feedback cycle to study the spatial structures. This analysis is accompanied by the proper orthogonal decomposition (POD) data, which reveals the presence of discrete vortices along the shear layer. The POD analysis is performed in both the spanwise and streamwise planes to extract the coherence in flow structures. Finally, dynamic mode decomposition is performed on the data sequence to obtain the dynamic information and deeper insight into the self-sustained mechanism.

De-biasing the dynamic mode decomposition for applied Koopman spectral analysis of noisy datasets

NASA Astrophysics Data System (ADS)

Hemati, Maziar S.; Rowley, Clarence W.; Deem, Eric A.; Cattafesta, Louis N.

2017-08-01

The dynamic mode decomposition (DMD)—a popular method for performing data-driven Koopman spectral analysis—has gained increased popularity for extracting dynamically meaningful spatiotemporal descriptions of fluid flows from snapshot measurements. Often times, DMD descriptions can be used for predictive purposes as well, which enables informed decision-making based on DMD model forecasts. Despite its widespread use and utility, DMD can fail to yield accurate dynamical descriptions when the measured snapshot data are imprecise due to, e.g., sensor noise. Here, we express DMD as a two-stage algorithm in order to isolate a source of systematic error. We show that DMD's first stage, a subspace projection step, systematically introduces bias errors by processing snapshots asymmetrically. To remove this systematic error, we propose utilizing an augmented snapshot matrix in a subspace projection step, as in problems of total least-squares, in order to account for the error present in all snapshots. The resulting unbiased and noise-aware total DMD (TDMD) formulation reduces to standard DMD in the absence of snapshot errors, while the two-stage perspective generalizes the de-biasing framework to other related methods as well. TDMD's performance is demonstrated in numerical and experimental fluids examples. In particular, in the analysis of time-resolved particle image velocimetry data for a separated flow, TDMD outperforms standard DMD by providing dynamical interpretations that are consistent with alternative analysis techniques. Further, TDMD extracts modes that reveal detailed spatial structures missed by standard DMD.

Intrinsic Multi-Scale Dynamic Behaviors of Complex Financial Systems.

PubMed

Ouyang, Fang-Yan; Zheng, Bo; Jiang, Xiong-Fei

2015-01-01

The empirical mode decomposition is applied to analyze the intrinsic multi-scale dynamic behaviors of complex financial systems. In this approach, the time series of the price returns of each stock is decomposed into a small number of intrinsic mode functions, which represent the price motion from high frequency to low frequency. These intrinsic mode functions are then grouped into three modes, i.e., the fast mode, medium mode and slow mode. The probability distribution of returns and auto-correlation of volatilities for the fast and medium modes exhibit similar behaviors as those of the full time series, i.e., these characteristics are rather robust in multi time scale. However, the cross-correlation between individual stocks and the return-volatility correlation are time scale dependent. The structure of business sectors is mainly governed by the fast mode when returns are sampled at a couple of days, while by the medium mode when returns are sampled at dozens of days. More importantly, the leverage and anti-leverage effects are dominated by the medium mode.

A low dimensional dynamical system for the wall layer

NASA Technical Reports Server (NTRS)

Aubry, N.; Keefe, L. R.

1987-01-01

Low dimensional dynamical systems which model a fully developed turbulent wall layer were derived.The model is based on the optimally fast convergent proper orthogonal decomposition, or Karhunen-Loeve expansion. This decomposition provides a set of eigenfunctions which are derived from the autocorrelation tensor at zero time lag. Via Galerkin projection, low dimensional sets of ordinary differential equations in time, for the coefficients of the expansion, were derived from the Navier-Stokes equations. The energy loss to the unresolved modes was modeled by an eddy viscosity representation, analogous to Heisenberg's spectral model. A set of eigenfunctions and eigenvalues were obtained from direct numerical simulation of a plane channel at a Reynolds number of 6600, based on the mean centerline velocity and the channel width flow and compared with previous work done by Herzog. Using the new eigenvalues and eigenfunctions, a new ten dimensional set of ordinary differential equations were derived using five non-zero cross-stream Fourier modes with a periodic length of 377 wall units. The dynamical system was integrated for a range of the eddy viscosity prameter alpha. This work is encouraging.

Application of Dynamic Mode Decomposition: Temporal Evolution of Flow Structures in an Aneurysm

NASA Astrophysics Data System (ADS)

Conlin, William; Yu, Paulo; Durgesh, Vibhav

2017-11-01

An aneurysm is an enlargement of a weakened arterial wall that can be fatal or debilitating on rupture. Aneurysm hemodynamics is integral to developing an understanding of aneurysm formation, growth, and rupture. The flow in an aneurysm exhibits complex fluid dynamics behavior due to an inherent unsteady inflow condition and its interactions with large-scale flow structures present in the aneurysm. The objective of this study is to identify the large-scale structures in the aneurysm, study temporal behavior, and quantify their interaction with the inflow condition. For this purpose, detailed Particle Image Velocimetry (PIV) measurements were performed at the center plane of an idealized aneurysm model for a range of inflow conditions. Inflow conditions were precisely controlled using a ViVitro SuperPump system. Dynamic Modal Decomposition (DMD) of the velocity field was used to identify coherent structures and their temporal behavior. DMD was successful in capturing the large-scale flow structures and their temporal behavior. A low dimensional approximation to the flow field was obtained with the most relevant dynamic modes and was used to obtain temporal information about the coherent structures and their interaction with the inflow, formation, evolution, and growth.

An improved EMD method for modal identification and a combined static-dynamic method for damage detection

NASA Astrophysics Data System (ADS)

Yang, Jinping; Li, Peizhen; Yang, Youfa; Xu, Dian

2018-04-01

Empirical mode decomposition (EMD) is a highly adaptable signal processing method. However, the EMD approach has certain drawbacks, including distortions from end effects and mode mixing. In the present study, these two problems are addressed using an end extension method based on the support vector regression machine (SVRM) and a modal decomposition method based on the characteristics of the Hilbert transform. The algorithm includes two steps: using the SVRM, the time series data are extended at both endpoints to reduce the end effects, and then, a modified EMD method using the characteristics of the Hilbert transform is performed on the resulting signal to reduce mode mixing. A new combined static-dynamic method for identifying structural damage is presented. This method combines the static and dynamic information in an equilibrium equation that can be solved using the Moore-Penrose generalized matrix inverse. The combination method uses the differences in displacements of the structure with and without damage and variations in the modal force vector. Tests on a four-story, steel-frame structure were conducted to obtain static and dynamic responses of the structure. The modal parameters are identified using data from the dynamic tests and improved EMD method. The new method is shown to be more accurate and effective than the traditional EMD method. Through tests with a shear-type test frame, the higher performance of the proposed static-dynamic damage detection approach, which can detect both single and multiple damage locations and the degree of the damage, is demonstrated. For structures with multiple damage, the combined approach is more effective than either the static or dynamic method. The proposed EMD method and static-dynamic damage detection method offer improved modal identification and damage detection, respectively, in structures.

Approaches for Subgrid Parameterization: Does Scaling Help?

NASA Astrophysics Data System (ADS)

Yano, Jun-Ichi

2016-04-01

Arguably the scaling behavior is a well-established fact in many geophysical systems. There are already many theoretical studies elucidating this issue. However, the scaling law is slow to be introduced in "operational" geophysical modelling, notably for weather forecast as well as climate projection models. The main purpose of this presentation is to ask why, and try to answer this question. As a reference point, the presentation reviews the three major approaches for traditional subgrid parameterization: moment, PDF (probability density function), and mode decomposition. The moment expansion is a standard method for describing the subgrid-scale turbulent flows both in the atmosphere and the oceans. The PDF approach is intuitively appealing as it directly deals with a distribution of variables in subgrid scale in a more direct manner. The third category, originally proposed by Aubry et al (1988) in context of the wall boundary-layer turbulence, is specifically designed to represent coherencies in compact manner by a low--dimensional dynamical system. Their original proposal adopts the proper orthogonal decomposition (POD, or empirical orthogonal functions, EOF) as their mode-decomposition basis. However, the methodology can easily be generalized into any decomposition basis. The mass-flux formulation that is currently adopted in majority of atmospheric models for parameterizing convection can also be considered a special case of the mode decomposition, adopting the segmentally-constant modes for the expansion basis. The mode decomposition can, furthermore, be re-interpreted as a type of Galarkin approach for numerically modelling the subgrid-scale processes. Simple extrapolation of this re-interpretation further suggests us that the subgrid parameterization problem may be re-interpreted as a type of mesh-refinement problem in numerical modelling. We furthermore see a link between the subgrid parameterization and downscaling problems along this line. The mode decomposition approach would also be the best framework for linking between the traditional parameterizations and the scaling perspectives. However, by seeing the link more clearly, we also see strength and weakness of introducing the scaling perspectives into parameterizations. Any diagnosis under a mode decomposition would immediately reveal a power-law nature of the spectrum. However, exploiting this knowledge in operational parameterization would be a different story. It is symbolic to realize that POD studies have been focusing on representing the largest-scale coherency within a grid box under a high truncation. This problem is already hard enough. Looking at differently, the scaling law is a very concise manner for characterizing many subgrid-scale variabilities in systems. We may even argue that the scaling law can provide almost complete subgrid-scale information in order to construct a parameterization, but with a major missing link: its amplitude must be specified by an additional condition. The condition called "closure" in the parameterization problem, and known to be a tough problem. We should also realize that the studies of the scaling behavior tend to be statistical in the sense that it hardly provides complete information for constructing a parameterization: can we specify the coefficients of all the decomposition modes by a scaling law perfectly when the first few leading modes are specified? Arguably, the renormalization group (RNG) is a very powerful tool for reducing a system with a scaling behavior into a low dimension, say, under an appropriate mode decomposition procedure. However, RNG is analytical tool: it is extremely hard to apply it to real complex geophysical systems. It appears that it is still a long way to go for us before we can begin to exploit the scaling law in order to construct operational subgrid parameterizations in effective manner.

xEMD procedures as a data - Assisted filtering method

NASA Astrophysics Data System (ADS)

Machrowska, Anna; Jonak, Józef

2018-01-01

The article presents the possibility of using Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD), Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Improved Complete Ensemble Empirical Mode Decomposition (ICEEMD) algorithms for mechanical system condition monitoring applications. There were presented the results of the xEMD procedures used for vibration signals of system in different states of wear.

Dynamic Factorization in Large-Scale Optimization

DTIC Science & Technology

1993-03-12

variable production charges, distribution via multiple modes, taxes, duties and duty drawback, and inventory charges. See Harrison, Arntzen , and Brown...Decomposition," presented at CORS/TIMS/ORSA meeting, Vancouver. British Columbia, Canada, May. Harrison, T. P., Arntzen , B. C., and Brown, G. G. 1992

On the need of mode interpolation for data-driven Galerkin models of a transient flow around a sphere

NASA Astrophysics Data System (ADS)

Stankiewicz, Witold; Morzyński, Marek; Kotecki, Krzysztof; Noack, Bernd R.

2017-04-01

We present a low-dimensional Galerkin model with state-dependent modes capturing linear and nonlinear dynamics. Departure point is a direct numerical simulation of the three-dimensional incompressible flow around a sphere at Reynolds numbers 400. This solution starts near the unstable steady Navier-Stokes solution and converges to a periodic limit cycle. The investigated Galerkin models are based on the dynamic mode decomposition (DMD) and derive the dynamical system from first principles, the Navier-Stokes equations. A DMD model with training data from the initial linear transient fails to predict the limit cycle. Conversely, a model from limit-cycle data underpredicts the initial growth rate roughly by a factor 5. Key enablers for uniform accuracy throughout the transient are a continuous mode interpolation between both oscillatory fluctuations and the addition of a shift mode. This interpolated model is shown to capture both the transient growth of the oscillation and the limit cycle.

Dynamic Factorization in Large-Scale Optimization

DTIC Science & Technology

1994-01-01

and variable production charges, distribution via multiple modes, taxes, duties and duty draw- back, and inventory charges. See Harrison, Arntzen and...34 Capital allocation and project selection via decomposition:’ presented at CORS/TIMS/ORSA meeting. Vancouver. Be ( 1989). T.P. Harrison. B.C. Arntzen and

Time-frequency analysis : mathematical analysis of the empirical mode decomposition.

DOT National Transportation Integrated Search

2009-01-01

Invented over 10 years ago, empirical mode : decomposition (EMD) provides a nonlinear : time-frequency analysis with the ability to successfully : analyze nonstationary signals. Mathematical : Analysis of the Empirical Mode Decomposition : is a...

Data-Driven Model Reduction and Transfer Operator Approximation

NASA Astrophysics Data System (ADS)

Klus, Stefan; Nüske, Feliks; Koltai, Péter; Wu, Hao; Kevrekidis, Ioannis; Schütte, Christof; Noé, Frank

2018-06-01

In this review paper, we will present different data-driven dimension reduction techniques for dynamical systems that are based on transfer operator theory as well as methods to approximate transfer operators and their eigenvalues, eigenfunctions, and eigenmodes. The goal is to point out similarities and differences between methods developed independently by the dynamical systems, fluid dynamics, and molecular dynamics communities such as time-lagged independent component analysis, dynamic mode decomposition, and their respective generalizations. As a result, extensions and best practices developed for one particular method can be carried over to other related methods.

Handling Qualities of Large Flexible Aircraft. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Poopaka, S.

1980-01-01

The effects on handling qualities of elastic modes interaction with the rigid body dynamics of a large flexible aircraft are studied by a mathematical computer simulation. An analytical method to predict the pilot ratings when there is a severe modes interactions is developed. This is done by extending the optimal control model of the human pilot response to include the mode decomposition mechanism into the model. The handling qualities are determined for a longitudinal tracking task using a large flexible aircraft with parametric variations in the undamped natural frequencies of the two lowest frequency, symmetric elastic modes made to induce varying amounts of mode interaction.

Intrinsic Multi-Scale Dynamic Behaviors of Complex Financial Systems

PubMed Central

Ouyang, Fang-Yan; Zheng, Bo; Jiang, Xiong-Fei

2015-01-01

The empirical mode decomposition is applied to analyze the intrinsic multi-scale dynamic behaviors of complex financial systems. In this approach, the time series of the price returns of each stock is decomposed into a small number of intrinsic mode functions, which represent the price motion from high frequency to low frequency. These intrinsic mode functions are then grouped into three modes, i.e., the fast mode, medium mode and slow mode. The probability distribution of returns and auto-correlation of volatilities for the fast and medium modes exhibit similar behaviors as those of the full time series, i.e., these characteristics are rather robust in multi time scale. However, the cross-correlation between individual stocks and the return-volatility correlation are time scale dependent. The structure of business sectors is mainly governed by the fast mode when returns are sampled at a couple of days, while by the medium mode when returns are sampled at dozens of days. More importantly, the leverage and anti-leverage effects are dominated by the medium mode. PMID:26427063

Use of Direct Dynamics Simulations to Determine Unimolecular Reaction Paths and Arrhenius Parameters for Large Molecules.

PubMed

Yang, Li; Sun, Rui; Hase, William L

2011-11-08

In a previous study (J. Chem. Phys.2008, 129, 094701) it was shown that for a large molecule, with a total energy much greater than its barrier for decomposition and whose vibrational modes are harmonic oscillators, the expressions for the classical Rice-Ramsperger-Kassel-Marcus (RRKM) (i.e., RRK) and classical transition-state theory (TST) rate constants become equivalent. Using this relationship, a molecule's unimolecular rate constants versus temperature may be determined from chemical dynamics simulations of microcanonical ensembles for the molecule at different total energies. The simulation identifies the molecule's unimolecular pathways and their Arrhenius parameters. In the work presented here, this approach is used to study the thermal decomposition of CH3-NH-CH═CH-CH3, an important constituent in the polymer of cross-linked epoxy resins. Direct dynamics simulations, at the MP2/6-31+G* level of theory, were used to investigate the decomposition of microcanonical ensembles for this molecule. The Arrhenius A and Ea parameters determined from the direct dynamics simulation are in very good agreement with the TST Arrhenius parameters for the MP2/6-31+G* potential energy surface. The simulation method applied here may be particularly useful for large molecules with a multitude of decomposition pathways and whose transition states may be difficult to determine and have structures that are not readily obvious.

Characterization of Flow Dynamics and Reduced-Order Description of Experimental Two-Phase Pipe Flow

NASA Astrophysics Data System (ADS)

Viggiano, Bianca; SkjæRaasen, Olaf; Tutkun, Murat; Cal, Raul Bayoan

2017-11-01

Multiphase pipe flow is investigated using proper orthogonal decomposition for tomographic X-ray data, where holdup, cross sectional phase distributions and phase interface characteristics are obtained. Instantaneous phase fractions of dispersed flow and slug flow are analyzed and a reduced order dynamical description is generated. The dispersed flow displays coherent structures in the first few modes near the horizontal center of the pipe, representing the liquid-liquid interface location while the slug flow case shows coherent structures that correspond to the cyclical formation and breakup of the slug in the first 10 modes. The reconstruction of the fields indicate that main features are observed in the low order dynamical descriptions utilizing less than 1 % of the full order model. POD temporal coefficients a1, a2 and a3 show interdependence for the slug flow case. The coefficients also describe the phase fraction holdup as a function of time for both dispersed and slug flow. These flows are highly applicable to petroleum transport pipelines, hydroelectric power and heat exchanger tubes to name a few. The mathematical representations obtained via proper orthogonal decomposition will deepen the understanding of fundamental multiphase flow characteristics.

Adaptive variational mode decomposition method for signal processing based on mode characteristic

NASA Astrophysics Data System (ADS)

Lian, Jijian; Liu, Zhuo; Wang, Haijun; Dong, Xiaofeng

2018-07-01

Variational mode decomposition is a completely non-recursive decomposition model, where all the modes are extracted concurrently. However, the model requires a preset mode number, which limits the adaptability of the method since a large deviation in the number of mode set will cause the discard or mixing of the mode. Hence, a method called Adaptive Variational Mode Decomposition (AVMD) was proposed to automatically determine the mode number based on the characteristic of intrinsic mode function. The method was used to analyze the simulation signals and the measured signals in the hydropower plant. Comparisons have also been conducted to evaluate the performance by using VMD, EMD and EWT. It is indicated that the proposed method has strong adaptability and is robust to noise. It can determine the mode number appropriately without modulation even when the signal frequencies are relatively close.

Effectiveness of Modal Decomposition for Tapping Atomic Force Microscopy Microcantilevers in Liquid Environment.

PubMed

Kim, Il Kwang; Lee, Soo Il

2016-05-01

The modal decomposition of tapping mode atomic force microscopy microcantilevers in liquid environments was studied experimentally. Microcantilevers with different lengths and stiffnesses and two sample surfaces with different elastic moduli were used in the experiment. The response modes of the microcantilevers were extracted as proper orthogonal modes through proper orthogonal decomposition. Smooth orthogonal decomposition was used to estimate the resonance frequency directly. The effects of the tapping setpoint and the elastic modulus of the sample under test were examined in terms of their multi-mode responses with proper orthogonal modes, proper orthogonal values, smooth orthogonal modes and smooth orthogonal values. Regardless of the stiffness of the microcantilever under test, the first mode was dominant in tapping mode atomic force microscopy under normal operating conditions. However, at lower tapping setpoints, the flexible microcantilever showed modal distortion and noise near the tip when tapping on a hard sample. The stiff microcantilever had a higher mode effect on a soft sample at lower tapping setpoints. Modal decomposition for tapping mode atomic force microscopy can thus be used to estimate the characteristics of samples in liquid environments.

Automated Identification of MHD Mode Bifurcation and Locking in Tokamaks

NASA Astrophysics Data System (ADS)

Riquezes, J. D.; Sabbagh, S. A.; Park, Y. S.; Bell, R. E.; Morton, L. A.

2017-10-01

Disruption avoidance is critical in reactor-scale tokamaks such as ITER to maintain steady plasma operation and avoid damage to device components. A key physical event chain that leads to disruptions is the appearance of rotating MHD modes, their slowing by resonant field drag mechanisms, and their locking. An algorithm has been developed that automatically detects bifurcation of the mode toroidal rotation frequency due to loss of torque balance under resonant braking, and mode locking for a set of shots using spectral decomposition. The present research examines data from NSTX, NSTX-U and KSTAR plasmas which differ significantly in aspect ratio (ranging from A = 1.3 - 3.5). The research aims to examine and compare the effectiveness of different algorithms for toroidal mode number discrimination, such as phase matching and singular value decomposition approaches, and to examine potential differences related to machine aspect ratio (e.g. mode eigenfunction shape variation). Simple theoretical models will be compared to the dynamics found. Main goals are to detect or potentially forecast the event chain early during a discharge. This would serve as a cue to engage active mode control or a controlled plasma shutdown. Supported by US DOE Contracts DE-SC0016614 and DE-AC02-09CH11466.

Simulation of transverse modes with their intrinsic Landau damping for bunched beams in the presence of space charge

DOE PAGES

Macridin, Alexandru; Burov, Alexey; Stern, Eric; ...

2015-07-22

Transverse dipole modes in bunches with space charge are simulated using the synergia accelerator modeling package and analyzed with dynamic mode decomposition. The properties of the first three space charge modes, including their shape, damping rates, and tune shifts are described over the entire range of space charge strength. As a result, the intrinsic Landau damping predicted and estimated in 2009 by one of the authors is confirmed with a reasonable scaling factor of ≃2.4. For the KV distribution, very good agreement with PATRIC simulations performed by Kornilov and Boine-Frankenheim is obtained.

Mode Analyses of Gyrokinetic Simulations of Plasma Microturbulence

NASA Astrophysics Data System (ADS)

Hatch, David R.

This thesis presents analysis of the excitation and role of damped modes in gyrokinetic simulations of plasma microturbulence. In order to address this question, mode decompositions are used to analyze gyrokinetic simulation data. A mode decomposition can be constructed by projecting a nonlinearly evolved gyrokinetic distribution function onto a set of linear eigenmodes, or alternatively by constructing a proper orthogonal decomposition of the distribution function. POD decompositions are used to examine the role of damped modes in saturating ion temperature gradient driven turbulence. In order to identify the contribution of different modes to the energy sources and sinks, numerical diagnostics for a gyrokinetic energy quantity were developed for the GENE code. The use of these energy diagnostics in conjunction with POD mode decompositions demonstrates that ITG turbulence saturates largely through dissipation by damped modes at the same perpendicular spatial scales as those of the driving instabilities. This defines a picture of turbulent saturation that is very different from both traditional hydrodynamic scenarios and also many common theories for the saturation of plasma turbulence. POD mode decompositions are also used to examine the role of subdominant modes in causing magnetic stochasticity in electromagnetic gyrokinetic simulations. It is shown that the magnetic stochasticity, which appears to be ubiquitous in electromagnetic microturbulence, is caused largely by subdominant modes with tearing parity. The application of higher-order singular value decomposition (HOSVD) to the full distribution function from gyrokinetic simulations is presented. This is an effort to demonstrate the ability to characterize and extract insight from a very large, complex, and high-dimensional data-set - the 5-D (plus time) gyrokinetic distribution function.

Dynamic Analysis and Control of Lightweight Manipulators with Flexible Parallel Link Mechanisms. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Lee, Jeh Won

1990-01-01

The objective is the theoretical analysis and the experimental verification of dynamics and control of a two link flexible manipulator with a flexible parallel link mechanism. Nonlinear equations of motion of the lightweight manipulator are derived by the Lagrangian method in symbolic form to better understand the structure of the dynamic model. The resulting equation of motion have a structure which is useful to reduce the number of terms calculated, to check correctness, or to extend the model to higher order. A manipulator with a flexible parallel link mechanism is a constrained dynamic system whose equations are sensitive to numerical integration error. This constrained system is solved using singular value decomposition of the constraint Jacobian matrix. Elastic motion is expressed by the assumed mode method. Mode shape functions of each link are chosen using the load interfaced component mode synthesis. The discrepancies between the analytical model and the experiment are explained using a simplified and a detailed finite element model.

Wind Farm Flow Modeling using an Input-Output Reduced-Order Model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Annoni, Jennifer; Gebraad, Pieter; Seiler, Peter

Wind turbines in a wind farm operate individually to maximize their own power regardless of the impact of aerodynamic interactions on neighboring turbines. There is the potential to increase power and reduce overall structural loads by properly coordinating turbines. To perform control design and analysis, a model needs to be of low computational cost, but retains the necessary dynamics seen in high-fidelity models. The objective of this work is to obtain a reduced-order model that represents the full-order flow computed using a high-fidelity model. A variety of methods, including proper orthogonal decomposition and dynamic mode decomposition, can be used tomore » extract the dominant flow structures and obtain a reduced-order model. In this paper, we combine proper orthogonal decomposition with a system identification technique to produce an input-output reduced-order model. This technique is used to construct a reduced-order model of the flow within a two-turbine array computed using a large-eddy simulation.« less

Acoustics flow analysis in circular duct using sound intensity and dynamic mode decomposition

NASA Astrophysics Data System (ADS)

Weyna, S.

2014-08-01

Sound intensity generation in hard-walled duct with acoustic flow (no mean-flow) is treated experimentally and shown graphically. In paper, numerous methods of visualization illustrating the vortex flow (2D, 3D) can graphically explain diffraction and scattering phenomena occurring inside the duct and around open end area. Sound intensity investigation in annular duct gives a physical picture of sound waves in any duct mode. In the paper, modal energy analysis are discussed with particular reference to acoustics acoustic orthogonal decomposition (AOD). The image of sound intensity fields before and above "cut-off" frequency region are found to compare acoustic modes which might resonate in duct. The experimental results show also the effects of axial and swirling flow. However acoustic field is extremely complicated, because pressures in non-propagating (cut-off) modes cooperate with the particle velocities in propagating modes, and vice versa. Measurement in cylindrical duct demonstrates also the cut-off phenomenon and the effect of reflection from open end. The aim of experimental study was to obtain information on low Mach number flows in ducts in order to improve physical understanding and validate theoretical CFD and CAA models that still may be improved.

Time-resolved SFG study of formate on a Ni( 1 1 1 ) surface under irradiation of picosecond laser pulses

NASA Astrophysics Data System (ADS)

Noguchi, H.; Okada, T.; Onda, K.; Kano, S. S.; Wada, A.; Domen, K.

2003-03-01

Time-resolved sum-frequency generation spectroscopy was carried out on a deuterated formate (DCOO) adsorbed on Ni(1 1 1) surface to investigate the surface reaction dynamics under instantaneous surface temperature jump induced by the irradiation by picosecond laser pulses. The irradiation of pump pulse (800 nm) caused the rapid intensity decrease of both CD and OCO stretching modes of bridged formate on Ni(1 1 1). Different temporal behaviors of intensity recovery between these two vibrational modes were observed, i.e., CD stretching mode recovered faster than OCO. This is the first result to show that the dynamics of adsorbates on metals strongly depends on the observed vibrational mode. From the results of temperature and pump fluence dependence, we concluded that the observed intensity change was not due to the decomposition or desorption, but was induced by a non-thermal process.

Fourier imaging of non-linear structure formation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brandbyge, Jacob; Hannestad, Steen, E-mail: jacobb@phys.au.dk, E-mail: sth@phys.au.dk

We perform a Fourier space decomposition of the dynamics of non-linear cosmological structure formation in ΛCDM models. From N -body simulations involving only cold dark matter we calculate 3-dimensional non-linear density, velocity divergence and vorticity Fourier realizations, and use these to calculate the fully non-linear mode coupling integrals in the corresponding fluid equations. Our approach allows for a reconstruction of the amount of mode coupling between any two wavenumbers as a function of redshift. With our Fourier decomposition method we identify the transfer of power from larger to smaller scales, the stable clustering regime, the scale where vorticity becomes important,more » and the suppression of the non-linear divergence power spectrum as compared to linear theory. Our results can be used to improve and calibrate semi-analytical structure formation models.« less

Control of complex dynamics and chaos in distributed parameter systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chakravarti, S.; Marek, M.; Ray, W.H.

This paper discusses a methodology for controlling complex dynamics and chaos in distributed parameter systems. The reaction-diffusion system with Brusselator kinetics, where the torus-doubling or quasi-periodic (two characteristic incommensurate frequencies) route to chaos exists in a defined range of parameter values, is used as an example. Poincare maps are used for characterization of quasi-periodic and chaotic attractors. The dominant modes or topos, which are inherent properties of the system, are identified by means of the Singular Value Decomposition. Tested modal feedback control schemas based on identified dominant spatial modes confirm the possibility of stabilization of simple quasi-periodic trajectories in themore » complex quasi-periodic or chaotic spatiotemporal patterns.« less

Interannual Variability and Trends of Extratropical Ozone. Part 1; Northern Hemisphere

NASA Technical Reports Server (NTRS)

Yung, Yuk L.

2008-01-01

The authors apply principal component analysis (PCA) to the extratropical total column ozone from the combined merged ozone data product and the European Centre for Medium-Range Weather Forecasts assimilated ozone from January 1979 to August 2002. The interannual variability (IAV) of extratropical O-3 in the Northern Hemisphere (NH) is characterized by four main modes. Attributable to dominant dynamical effects, these four modes account for nearly 60% of the total ozone variance in the NH. The patterns of variability are distinctly different from those derived for total O-3 in the tropics. To relate the derived patterns of O-3 to atmospheric dynamics, similar decompositions are performed for the 30 100-Wa geopotential thickness. The results reveal intimate connections between the IAV of total ozone and the atmospheric circulation. The first two leading modes are nearly zonally symmetric and represent the connections to the annular modes and the quasi-biennial oscillation. The other two modes exhibit in-quadrature, wavenumber-1 structures that, when combined, describe the displacement of the polar vortices in response to planetary waves. In the NH, the extrema of these combined modes have preferred locations that suggest fixed topographical and land-sea thermal forcing of the involved planetary waves. Similar spatial patterns and trends in extratropical column ozone are simulated by the Goddard Earth Observation System chemistryclimate model (GEOS-CCM). The decreasing O-3 trend is captured in the first mode. The largest trend occurs at the North Pole, with values similar to-1 Dobson Unit (DU) yr(-1). There is almost no trend in tropical O-3. The trends derived from PCA are confirmed using a completely independent method, empirical mode decomposition, for zonally averaged O-3 data. The O-3 trend is also captured by mode 1 in the GEOS-CCM, but the decrease is substantially larger than that in the real atmosphere.

A Structural Model Decomposition Framework for Hybrid Systems Diagnosis

NASA Technical Reports Server (NTRS)

Daigle, Matthew; Bregon, Anibal; Roychoudhury, Indranil

2015-01-01

Nowadays, a large number of practical systems in aerospace and industrial environments are best represented as hybrid systems that consist of discrete modes of behavior, each defined by a set of continuous dynamics. These hybrid dynamics make the on-line fault diagnosis task very challenging. In this work, we present a new modeling and diagnosis framework for hybrid systems. Models are composed from sets of user-defined components using a compositional modeling approach. Submodels for residual generation are then generated for a given mode, and reconfigured efficiently when the mode changes. Efficient reconfiguration is established by exploiting causality information within the hybrid system models. The submodels can then be used for fault diagnosis based on residual generation and analysis. We demonstrate the efficient causality reassignment, submodel reconfiguration, and residual generation for fault diagnosis using an electrical circuit case study.

Modified complementary ensemble empirical mode decomposition and intrinsic mode functions evaluation index for high-speed train gearbox fault diagnosis

NASA Astrophysics Data System (ADS)

Chen, Dongyue; Lin, Jianhui; Li, Yanping

2018-06-01

Complementary ensemble empirical mode decomposition (CEEMD) has been developed for the mode-mixing problem in Empirical Mode Decomposition (EMD) method. Compared to the ensemble empirical mode decomposition (EEMD), the CEEMD method reduces residue noise in the signal reconstruction. Both CEEMD and EEMD need enough ensemble number to reduce the residue noise, and hence it would be too much computation cost. Moreover, the selection of intrinsic mode functions (IMFs) for further analysis usually depends on experience. A modified CEEMD method and IMFs evaluation index are proposed with the aim of reducing the computational cost and select IMFs automatically. A simulated signal and in-service high-speed train gearbox vibration signals are employed to validate the proposed method in this paper. The results demonstrate that the modified CEEMD can decompose the signal efficiently with less computation cost, and the IMFs evaluation index can select the meaningful IMFs automatically.

Trend extraction using empirical mode decomposition and statistical empirical mode decomposition: Case study: Kuala Lumpur stock market

NASA Astrophysics Data System (ADS)

Jaber, Abobaker M.

2014-12-01

Two nonparametric methods for prediction and modeling of financial time series signals are proposed. The proposed techniques are designed to handle non-stationary and non-linearity behave and to extract meaningful signals for reliable prediction. Due to Fourier Transform (FT), the methods select significant decomposed signals that will be employed for signal prediction. The proposed techniques developed by coupling Holt-winter method with Empirical Mode Decomposition (EMD) and it is Extending the scope of empirical mode decomposition by smoothing (SEMD). To show performance of proposed techniques, we analyze daily closed price of Kuala Lumpur stock market index.

Dynamic Bayesian wavelet transform: New methodology for extraction of repetitive transients

NASA Astrophysics Data System (ADS)

Wang, Dong; Tsui, Kwok-Leung

2017-05-01

Thanks to some recent research works, dynamic Bayesian wavelet transform as new methodology for extraction of repetitive transients is proposed in this short communication to reveal fault signatures hidden in rotating machine. The main idea of the dynamic Bayesian wavelet transform is to iteratively estimate posterior parameters of wavelet transform via artificial observations and dynamic Bayesian inference. First, a prior wavelet parameter distribution can be established by one of many fast detection algorithms, such as the fast kurtogram, the improved kurtogram, the enhanced kurtogram, the sparsogram, the infogram, continuous wavelet transform, discrete wavelet transform, wavelet packets, multiwavelets, empirical wavelet transform, empirical mode decomposition, local mean decomposition, etc.. Second, artificial observations can be constructed based on one of many metrics, such as kurtosis, the sparsity measurement, entropy, approximate entropy, the smoothness index, a synthesized criterion, etc., which are able to quantify repetitive transients. Finally, given artificial observations, the prior wavelet parameter distribution can be posteriorly updated over iterations by using dynamic Bayesian inference. More importantly, the proposed new methodology can be extended to establish the optimal parameters required by many other signal processing methods for extraction of repetitive transients.

Dynamic Stability Analysis of Linear Time-varying Systems via an Extended Modal Identification Approach

NASA Astrophysics Data System (ADS)

Ma, Zhisai; Liu, Li; Zhou, Sida; Naets, Frank; Heylen, Ward; Desmet, Wim

2017-03-01

The problem of linear time-varying(LTV) system modal analysis is considered based on time-dependent state space representations, as classical modal analysis of linear time-invariant systems and current LTV system modal analysis under the "frozen-time" assumption are not able to determine the dynamic stability of LTV systems. Time-dependent state space representations of LTV systems are first introduced, and the corresponding modal analysis theories are subsequently presented via a stability-preserving state transformation. The time-varying modes of LTV systems are extended in terms of uniqueness, and are further interpreted to determine the system's stability. An extended modal identification is proposed to estimate the time-varying modes, consisting of the estimation of the state transition matrix via a subspace-based method and the extraction of the time-varying modes by the QR decomposition. The proposed approach is numerically validated by three numerical cases, and is experimentally validated by a coupled moving-mass simply supported beam experimental case. The proposed approach is capable of accurately estimating the time-varying modes, and provides a new way to determine the dynamic stability of LTV systems by using the estimated time-varying modes.

Combined Molecular and Spin Dynamics Simulation of Lattice Vacancies in BCC Iron

NASA Astrophysics Data System (ADS)

Mudrick, Mark; Perera, Dilina; Eisenbach, Markus; Landau, David P.

Using an atomistic model that treats translational and spin degrees of freedom equally, combined molecular and spin dynamics simulations have been performed to study dynamic properties of BCC iron at varying levels of defect impurity. Atomic interactions are described by an empirical many-body potential, and spin interactions with a Heisenberg-like Hamiltonian with a coordinate dependent exchange interaction. Equations of motion are solved numerically using the second-order Suzuki-Trotter decomposition for the time evolution operator. We analyze the spatial and temporal correlation functions for atomic displacements and magnetic order to obtain the effect of vacancy defects on the phonon and magnon excitations. We show that vacancy clusters in the material cause splitting of the characteristic transverse spin-wave excitations, indicating the production of additional excitation modes. Additionally, we investigate the coupling of the atomic and magnetic modes. These modes become more distinct with increasing vacancy cluster size. This material is based upon work supported by the U.S. Department of Energy Office of Science Graduate Student Research (SCGSR) program.

Stability and modal analysis of shock/boundary layer interactions

NASA Astrophysics Data System (ADS)

Nichols, Joseph W.; Larsson, Johan; Bernardini, Matteo; Pirozzoli, Sergio

2017-02-01

The dynamics of oblique shock wave/turbulent boundary layer interactions is analyzed by mining a large-eddy simulation (LES) database for various strengths of the incoming shock. The flow dynamics is first analyzed by means of dynamic mode decomposition (DMD), which highlights the simultaneous occurrence of two types of flow modes, namely a low-frequency type associated with breathing motion of the separation bubble, accompanied by flapping motion of the reflected shock, and a high-frequency type associated with the propagation of instability waves past the interaction zone. Global linear stability analysis performed on the mean LES flow fields yields a single unstable zero-frequency mode, plus a variety of marginally stable low-frequency modes whose stability margin decreases with the strength of the interaction. The least stable linear modes are grouped into two classes, one of which bears striking resemblance to the breathing mode recovered from DMD and another class associated with revolving motion within the separation bubble. The results of the modal and linear stability analysis support the notion that low-frequency dynamics is intrinsic to the interaction zone, but some continuous forcing from the upstream boundary layer may be required to keep the system near a limit cycle. This can be modeled as a weakly damped oscillator with forcing, as in the early empirical model by Plotkin (AIAA J 13:1036-1040, 1975).

Numerical computation of linear instability of detonations

NASA Astrophysics Data System (ADS)

Kabanov, Dmitry; Kasimov, Aslan

2017-11-01

We propose a method to study linear stability of detonations by direct numerical computation. The linearized governing equations together with the shock-evolution equation are solved in the shock-attached frame using a high-resolution numerical algorithm. The computed results are processed by the Dynamic Mode Decomposition technique to generate dispersion relations. The method is applied to the reactive Euler equations with simple-depletion chemistry as well as more complex multistep chemistry. The results are compared with those known from normal-mode analysis. We acknowledge financial support from King Abdullah University of Science and Technology.

Structure of local interactions in complex financial dynamics

PubMed Central

Jiang, X. F.; Chen, T. T.; Zheng, B.

2014-01-01

With the network methods and random matrix theory, we investigate the interaction structure of communities in financial markets. In particular, based on the random matrix decomposition, we clarify that the local interactions between the business sectors (subsectors) are mainly contained in the sector mode. In the sector mode, the average correlation inside the sectors is positive, while that between the sectors is negative. Further, we explore the time evolution of the interaction structure of the business sectors, and observe that the local interaction structure changes dramatically during a financial bubble or crisis. PMID:24936906

Aeroelastic System Development Using Proper Orthogonal Decomposition and Volterra Theory

NASA Technical Reports Server (NTRS)

Lucia, David J.; Beran, Philip S.; Silva, Walter A.

2003-01-01

This research combines Volterra theory and proper orthogonal decomposition (POD) into a hybrid methodology for reduced-order modeling of aeroelastic systems. The out-come of the method is a set of linear ordinary differential equations (ODEs) describing the modal amplitudes associated with both the structural modes and the POD basis functions for the uid. For this research, the structural modes are sine waves of varying frequency, and the Volterra-POD approach is applied to the fluid dynamics equations. The structural modes are treated as forcing terms which are impulsed as part of the uid model realization. Using this approach, structural and uid operators are coupled into a single aeroelastic operator. This coupling converts a free boundary uid problem into an initial value problem, while preserving the parameter (or parameters) of interest for sensitivity analysis. The approach is applied to an elastic panel in supersonic cross ow. The hybrid Volterra-POD approach provides a low-order uid model in state-space form. The linear uid model is tightly coupled with a nonlinear panel model using an implicit integration scheme. The resulting aeroelastic model provides correct limit-cycle oscillation prediction over a wide range of panel dynamic pressure values. Time integration of the reduced-order aeroelastic model is four orders of magnitude faster than the high-order solution procedure developed for this research using traditional uid and structural solvers.

Artifact removal from EEG data with empirical mode decomposition

NASA Astrophysics Data System (ADS)

Grubov, Vadim V.; Runnova, Anastasiya E.; Efremova, Tatyana Yu.; Hramov, Alexander E.

2017-03-01

In the paper we propose the novel method for dealing with the physiological artifacts caused by intensive activity of facial and neck muscles and other movements in experimental human EEG recordings. The method is based on analysis of EEG signals with empirical mode decomposition (Hilbert-Huang transform). We introduce the mathematical algorithm of the method with following steps: empirical mode decomposition of EEG signal, choosing of empirical modes with artifacts, removing empirical modes with artifacts, reconstruction of the initial EEG signal. We test the method on filtration of experimental human EEG signals from movement artifacts and show high efficiency of the method.

Modal Structures in flow past a cylinder

NASA Astrophysics Data System (ADS)

Murshed, Mohammad

2017-11-01

With the advent of data, there have been opportunities to apply formalism to detect patterns or simple relations. For instance, a phenomenon can be defined through a partial differential equation which may not be very useful right away, whereas a formula for the evolution of a primary variable may be interpreted quite easily. Having access to data is not enough to move on since doing advanced linear algebra can put strain on the way computations are being done. A canonical problem in the field of aerodynamics is the transient flow past a cylinder where the viscosity can be adjusted to set the Reynolds number (Re). We observe the effect of the critical Re on the certain modes of behavior in time scale. A 2D-velocity field works as an input to analyze the modal structure of the flow using the Proper Orthogonal Decomposition and Koopman Mode/Dynamic Mode Decomposition. This will enable prediction of the solution further in time (taking into account the dependence on Re) and help us evaluate and discuss the associated error in the mechanism.

Photoinduced discommensuration of the commensurate charge-density wave phase in 1 T -Ta S2

NASA Astrophysics Data System (ADS)

Tanimura, Katsumi

2018-06-01

The dynamics induced by femtosecond-laser excitation of the commensurate phase of the charge-density wave (CDW) in 1 T -Ta S2 have been studied using both time-resolved electron diffraction and the time-resolved spectroscopy of coherent-phonon dynamics. Electron diffraction results show that the commensurate CDW phase is transformed into a new phase with CDW order that is similar to the nearly commensurate phase with threshold-type transition rates; the threshold excitation density of 0.2 per 13 Ta atoms is evaluated. Coherent-phonon spectroscopy results show that, together with the amplitude mode of CDW with a frequency of 2.41 THz, two other modes with frequencies of 2.34 and 2.07 THz are excited in the photoexcited commensurate CDW phase over a timescale of several tens of picoseconds after excitation. Spectroscopic, temporal, and excitation-intensity dependent characteristics of the three coherent phonons reveal that a photoinduced decomposition of the commensurate CDW order into an ensemble of domains with different CDW orders is induced before the CDW-phase transition occurs. The physics underlying the photoinduced decomposition and evolution into discommensurations responsible for the CDW-order transformation are discussed.

Exploring the Common Dynamics of Homologous Proteins. Application to the Globin Family

PubMed Central

Maguid, Sandra; Fernandez-Alberti, Sebastian; Ferrelli, Leticia; Echave, Julian

2005-01-01

We present a procedure to explore the global dynamics shared between members of the same protein family. The method allows the comparison of patterns of vibrational motion obtained by Gaussian network model analysis. After the identification of collective coordinates that were conserved during evolution, we quantify the common dynamics within a family. Representative vectors that describe these dynamics are defined using a singular value decomposition approach. As a test case, the globin heme-binding family is considered. The two lowest normal modes are shown to be conserved within this family. Our results encourage the development of models for protein evolution that take into account the conservation of dynamical features. PMID:15749782

Dynamics of flow control in an emulated boundary layer-ingesting offset diffuser

NASA Astrophysics Data System (ADS)

Gissen, A. N.; Vukasinovic, B.; Glezer, A.

2014-08-01

Dynamics of flow control comprised of arrays of active (synthetic jets) and passive (vanes) control elements , and its effectiveness for suppression of total-pressure distortion is investigated experimentally in an offset diffuser, in the absence of internal flow separation. The experiments are conducted in a wind tunnel inlet model at speeds up to M = 0.55 using approach flow conditioning that mimics boundary layer ingestion on a Blended-Wing-Body platform. Time-dependent distortion of the dynamic total-pressure field at the `engine face' is measured using an array of forty total-pressure probes, and the control-induced distortion changes are analyzed using triple decomposition and proper orthogonal decomposition (POD). These data indicate that an array of the flow control small-scale synthetic jet vortices merge into two large-scale, counter-rotating streamwise vortices that exert significant changes in the flow distortion. The two most energetic POD modes appear to govern the distortion dynamics in either active or hybrid flow control approaches. Finally, it is shown that the present control approach is sufficiently robust to reduce distortion with different inlet conditions of the baseline flow.

Modes of asymmetry: The application of harmonic analysis to symmetric quantum dynamics and quantum reference frames

NASA Astrophysics Data System (ADS)

Marvian, Iman; Spekkens, Robert W.

2014-12-01

Finding the consequences of symmetry for open-system quantum dynamics is a problem with broad applications, including describing thermal relaxation, deriving quantum limits on the performance of amplifiers, and exploring quantum metrology in the presence of noise. The symmetry of the dynamics may reflect a symmetry of the fundamental laws of nature or a symmetry of a low-energy effective theory, or it may describe a practical restriction such as the lack of a reference frame. In this paper, we apply some tools of harmonic analysis together with ideas from quantum information theory to this problem. The central idea is to study the decomposition of quantum operations—in particular, states, measurements, and channels—into different modes, which we call modes of asymmetry. Under symmetric processing, a given mode of the input is mapped to the corresponding mode of the output, implying that one can only generate a given output if the input contains all of the necessary modes. By defining monotones that quantify the asymmetry in a particular mode, we also derive quantitative constraints on the resources of asymmetry that are required to simulate a given asymmetric operation. We present applications of our results for deriving bounds on the probability of success in nondeterministic state transitions, such as quantum amplification, and a simplified formalism for studying the degradation of quantum reference frames.

Data-driven sensor placement from coherent fluid structures

NASA Astrophysics Data System (ADS)

Manohar, Krithika; Kaiser, Eurika; Brunton, Bingni W.; Kutz, J. Nathan; Brunton, Steven L.

2017-11-01

Optimal sensor placement is a central challenge in the prediction, estimation and control of fluid flows. We reinterpret sensor placement as optimizing discrete samples of coherent fluid structures for full state reconstruction. This permits a drastic reduction in the number of sensors required for faithful reconstruction, since complex fluid interactions can often be described by a small number of coherent structures. Our work optimizes point sensors using the pivoted matrix QR factorization to sample coherent structures directly computed from flow data. We apply this sampling technique in conjunction with various data-driven modal identification methods, including the proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD). In contrast to POD-based sensors, DMD demonstrably enables the optimization of sensors for prediction in systems exhibiting multiple scales of dynamics. Finally, reconstruction accuracy from pivot sensors is shown to be competitive with sensors obtained using traditional computationally prohibitive optimization methods.

Characterizing Feedback Control Mechanisms in Nonlinear Microbial Models of Soil Organic Matter Decomposition by Stability Analysis

NASA Astrophysics Data System (ADS)

Georgiou, K.; Tang, J.; Riley, W. J.; Torn, M. S.

2014-12-01

Soil organic matter (SOM) decomposition is regulated by biotic and abiotic processes. Feedback interactions between such processes may act to dampen oscillatory responses to perturbations from equilibrium. Indeed, although biological oscillations have been observed in small-scale laboratory incubations, the overlying behavior at the plot-scale exhibits a relatively stable response to disturbances in input rates and temperature. Recent studies have demonstrated the ability of microbial models to capture nonlinear feedbacks in SOM decomposition that linear Century-type models are unable to reproduce, such as soil priming in response to increased carbon input. However, these microbial models often exhibit strong oscillatory behavior that is deemed unrealistic. The inherently nonlinear dynamics of SOM decomposition have important implications for global climate-carbon and carbon-concentration feedbacks. It is therefore imperative to represent these dynamics in Earth System Models (ESMs) by introducing sub-models that accurately represent microbial and abiotic processes. In the present study we explore, both analytically and numerically, four microbe-enabled model structures of varying levels of complexity. The most complex model combines microbial physiology, a non-linear mineral sorption isotherm, and enzyme dynamics. Based on detailed stability analysis of the nonlinear dynamics, we calculate the system modes as functions of model parameters. This dependence provides insight into the source of state oscillations. We find that feedback mechanisms that emerge from careful representation of enzyme and mineral interactions, with parameter values in a prescribed range, are critical for both maintaining system stability and capturing realistic responses to disturbances. Corroborating and expanding upon the results of recent studies, we explain the emergence of oscillatory responses and discuss the appropriate microbe-enabled model structure for inclusion in ESMs.

Non-Linear Structural Dynamics Characterization using a Scanning Laser Vibrometer

NASA Technical Reports Server (NTRS)

Pai, P. F.; Lee, S.-Y.

2003-01-01

This paper presents the use of a scanning laser vibrometer and a signal decomposition method to characterize non-linear dynamics of highly flexible structures. A Polytec PI PSV-200 scanning laser vibrometer is used to measure transverse velocities of points on a structure subjected to a harmonic excitation. Velocity profiles at different times are constructed using the measured velocities, and then each velocity profile is decomposed using the first four linear mode shapes and a least-squares curve-fitting method. From the variations of the obtained modal \\ielocities with time we search for possible non-linear phenomena. A cantilevered titanium alloy beam subjected to harmonic base-excitations around the second. third, and fourth natural frequencies are examined in detail. Influences of the fixture mass. gravity. mass centers of mode shapes. and non-linearities are evaluated. Geometrically exact equations governing the planar, harmonic large-amplitude vibrations of beams are solved for operational deflection shapes using the multiple shooting method. Experimental results show the existence of 1:3 and 1:2:3 external and internal resonances. energy transfer from high-frequency modes to the first mode. and amplitude- and phase- modulation among several modes. Moreover, the existence of non-linear normal modes is found to be questionable.

Extended dynamic mode decomposition with dictionary learning: A data-driven adaptive spectral decomposition of the Koopman operator

NASA Astrophysics Data System (ADS)

Li, Qianxiao; Dietrich, Felix; Bollt, Erik M.; Kevrekidis, Ioannis G.

2017-10-01

Numerical approximation methods for the Koopman operator have advanced considerably in the last few years. In particular, data-driven approaches such as dynamic mode decomposition (DMD)51 and its generalization, the extended-DMD (EDMD), are becoming increasingly popular in practical applications. The EDMD improves upon the classical DMD by the inclusion of a flexible choice of dictionary of observables which spans a finite dimensional subspace on which the Koopman operator can be approximated. This enhances the accuracy of the solution reconstruction and broadens the applicability of the Koopman formalism. Although the convergence of the EDMD has been established, applying the method in practice requires a careful choice of the observables to improve convergence with just a finite number of terms. This is especially difficult for high dimensional and highly nonlinear systems. In this paper, we employ ideas from machine learning to improve upon the EDMD method. We develop an iterative approximation algorithm which couples the EDMD with a trainable dictionary represented by an artificial neural network. Using the Duffing oscillator and the Kuramoto Sivashinsky partical differential equation as examples, we show that our algorithm can effectively and efficiently adapt the trainable dictionary to the problem at hand to achieve good reconstruction accuracy without the need to choose a fixed dictionary a priori. Furthermore, to obtain a given accuracy, we require fewer dictionary terms than EDMD with fixed dictionaries. This alleviates an important shortcoming of the EDMD algorithm and enhances the applicability of the Koopman framework to practical problems.

Extended dynamic mode decomposition with dictionary learning: A data-driven adaptive spectral decomposition of the Koopman operator.

PubMed

Li, Qianxiao; Dietrich, Felix; Bollt, Erik M; Kevrekidis, Ioannis G

2017-10-01

Numerical approximation methods for the Koopman operator have advanced considerably in the last few years. In particular, data-driven approaches such as dynamic mode decomposition (DMD) 51 and its generalization, the extended-DMD (EDMD), are becoming increasingly popular in practical applications. The EDMD improves upon the classical DMD by the inclusion of a flexible choice of dictionary of observables which spans a finite dimensional subspace on which the Koopman operator can be approximated. This enhances the accuracy of the solution reconstruction and broadens the applicability of the Koopman formalism. Although the convergence of the EDMD has been established, applying the method in practice requires a careful choice of the observables to improve convergence with just a finite number of terms. This is especially difficult for high dimensional and highly nonlinear systems. In this paper, we employ ideas from machine learning to improve upon the EDMD method. We develop an iterative approximation algorithm which couples the EDMD with a trainable dictionary represented by an artificial neural network. Using the Duffing oscillator and the Kuramoto Sivashinsky partical differential equation as examples, we show that our algorithm can effectively and efficiently adapt the trainable dictionary to the problem at hand to achieve good reconstruction accuracy without the need to choose a fixed dictionary a priori. Furthermore, to obtain a given accuracy, we require fewer dictionary terms than EDMD with fixed dictionaries. This alleviates an important shortcoming of the EDMD algorithm and enhances the applicability of the Koopman framework to practical problems.

Identification of Dynamic Patterns of Speech-Evoked Auditory Brainstem Response Based on Ensemble Empirical Mode Decomposition and Nonlinear Time Series Analysis Methods

NASA Astrophysics Data System (ADS)

Mozaffarilegha, Marjan; Esteki, Ali; Ahadi, Mohsen; Nazeri, Ahmadreza

The speech-evoked auditory brainstem response (sABR) shows how complex sounds such as speech and music are processed in the auditory system. Speech-ABR could be used to evaluate particular impairments and improvements in auditory processing system. Many researchers used linear approaches for characterizing different components of sABR signal, whereas nonlinear techniques are not applied so commonly. The primary aim of the present study is to examine the underlying dynamics of normal sABR signals. The secondary goal is to evaluate whether some chaotic features exist in this signal. We have presented a methodology for determining various components of sABR signals, by performing Ensemble Empirical Mode Decomposition (EEMD) to get the intrinsic mode functions (IMFs). Then, composite multiscale entropy (CMSE), the largest Lyapunov exponent (LLE) and deterministic nonlinear prediction are computed for each extracted IMF. EEMD decomposes sABR signal into five modes and a residue. The CMSE results of sABR signals obtained from 40 healthy people showed that 1st, and 2nd IMFs were similar to the white noise, IMF-3 with synthetic chaotic time series and 4th, and 5th IMFs with sine waveform. LLE analysis showed positive values for 3rd IMFs. Moreover, 1st, and 2nd IMFs showed overlaps with surrogate data and 3rd, 4th and 5th IMFs showed no overlap with corresponding surrogate data. Results showed the presence of noisy, chaotic and deterministic components in the signal which respectively corresponded to 1st, and 2nd IMFs, IMF-3, and 4th and 5th IMFs. While these findings provide supportive evidence of the chaos conjecture for the 3rd IMF, they do not confirm any such claims. However, they provide a first step towards an understanding of nonlinear behavior of auditory system dynamics in brainstem level.

Solar rotational modulations of spectral irradiance and correlations with the variability of total solar irradiance

NASA Astrophysics Data System (ADS)

Lee, Jae N.; Cahalan, Robert F.; Wu, Dong L.

2016-09-01

Aims: We characterize the solar rotational modulations of spectral solar irradiance (SSI) and compare them with the corresponding changes of total solar irradiance (TSI). Solar rotational modulations of TSI and SSI at wavelengths between 120 and 1600 nm are identified over one hundred Carrington rotational cycles during 2003-2013. Methods: The SORCE (Solar Radiation and Climate Experiment) and TIMED (Thermosphere Ionosphere Mesosphere Energetics and Dynamics)/SEE (Solar EUV Experiment) measured and SATIRE-S modeled solar irradiances are analyzed using the EEMD (Ensemble Empirical Mode Decomposition) method to determine the phase and amplitude of 27-day solar rotational variation in TSI and SSI. Results: The mode decomposition clearly identifies 27-day solar rotational variations in SSI between 120 and 1600 nm, and there is a robust wavelength dependence in the phase of the rotational mode relative to that of TSI. The rotational modes of visible (VIS) and near infrared (NIR) are in phase with the mode of TSI, but the phase of the rotational mode of ultraviolet (UV) exhibits differences from that of TSI. While it is questionable that the VIS to NIR portion of the solar spectrum has yet been observed with sufficient accuracy and precision to determine the 11-year solar cycle variations, the temporal variations over one hundred cycles of 27-day solar rotation, independent of the two solar cycles in which they are embedded, show distinct solar rotational modulations at each wavelength.

Solar Rotational Modulations of Spectral Irradiance and Correlations with the Variability of Total Solar Irradiance

NASA Technical Reports Server (NTRS)

Lee, Jae N.; Cahalan, Robert F.; Wu, Dong L.

2016-01-01

Aims: We characterize the solar rotational modulations of spectral solar irradiance (SSI) and compare them with the corresponding changes of total solar irradiance (TSI). Solar rotational modulations of TSI and SSI at wavelengths between 120 and 1600 nm are identified over one hundred Carrington rotational cycles during 2003-2013. Methods: The SORCE (Solar Radiation and Climate Experiment) and TIMED (Thermosphere Ionosphere Mesosphere Energetics and Dynamics)/SEE (Solar EUV Experiment) measured and SATIRE-S modeled solar irradiances are analyzed using the EEMD (Ensemble Empirical Mode Decomposition) method to determine the phase and amplitude of 27-day solar rotational variation in TSI and SSI. Results: The mode decomposition clearly identifies 27-day solar rotational variations in SSI between 120 and 1600 nm, and there is a robust wavelength dependence in the phase of the rotational mode relative to that of TSI. The rotational modes of visible (VIS) and near infrared (NIR) are in phase with the mode of TSI, but the phase of the rotational mode of ultraviolet (UV) exhibits differences from that of TSI. While it is questionable that the VIS to NIR portion of the solar spectrum has yet been observed with sufficient accuracy and precision to determine the 11-year solar cycle variations, the temporal variations over one hundred cycles of 27-day solar rotation, independent of the two solar cycles in which they are embedded, show distinct solar rotational modulations at each wavelength.

Comparison of two interpolation methods for empirical mode decomposition based evaluation of radiographic femur bone images.

PubMed

Udhayakumar, Ganesan; Sujatha, Chinnaswamy Manoharan; Ramakrishnan, Swaminathan

2013-01-01

Analysis of bone strength in radiographic images is an important component of estimation of bone quality in diseases such as osteoporosis. Conventional radiographic femur bone images are used to analyze its architecture using bi-dimensional empirical mode decomposition method. Surface interpolation of local maxima and minima points of an image is a crucial part of bi-dimensional empirical mode decomposition method and the choice of appropriate interpolation depends on specific structure of the problem. In this work, two interpolation methods of bi-dimensional empirical mode decomposition are analyzed to characterize the trabecular femur bone architecture of radiographic images. The trabecular bone regions of normal and osteoporotic femur bone images (N = 40) recorded under standard condition are used for this study. The compressive and tensile strength regions of the images are delineated using pre-processing procedures. The delineated images are decomposed into their corresponding intrinsic mode functions using interpolation methods such as Radial basis function multiquadratic and hierarchical b-spline techniques. Results show that bi-dimensional empirical mode decomposition analyses using both interpolations are able to represent architectural variations of femur bone radiographic images. As the strength of the bone depends on architectural variation in addition to bone mass, this study seems to be clinically useful.

Experimental investigation of a reacting transverse jet in a high pressure oscillating vitiated crossflow

NASA Astrophysics Data System (ADS)

Fugger, Christopher A.

Staged combustion is one design approach in a gas turbine engine to reduce pollutant emission levels. In axially staged combustion, portions of the air and fuel are injected downstream of a lean premixed low NOx primary combustion zone. The gas residence time at elevated temperatures is decreased resulting in lower thermal NOx, and the reduced oxygen and high temperature vitiated primary zone flow further help to reduce pollutant emissions and quickly complete combustion. One implementation of axially staged combustion is transverse fuel jet injection. An important consideration for staged combustion systems, though, is how the primary and secondary combustion zones can couple through the acoustic resonances of the chamber. These couplings can lead to additional source terms that pump energy into the resonant acoustic field and help sustain the high-amplitude combustor pressure oscillations. An understanding of these couplings is important so that it may be possible to design a secondary combustion system that provides inherent damping to the combustor system. To systematically characterize the coupling of a reacting jet in unsteady crossflow in detail, the effects of an an unsteady pressure flowfield and an unsteady velocity flowfield are separately investigated. An optically accessible resonant combustion chamber was designed and built as part of this work to generate a standing wave unsteady vitiated crossflow at a chamber pressure of 0.9 MPa. The location of transverse jet injection corresponds to one of two locations, where one location is the pressure node and the other location the pressure anti-node of the resonant chamber acoustic mode. The injection location is optically accessible, and the dynamic interactions between the transverse jet flow and the 1st and 2nd axial combustor modes are measured using 10 kHz OH-PLIF and 2D PIV. This document analyzes five test cases: two non-reacting jets and three reacting jets. All cases correspond to jet injection near a pressure node of the 1st axial combustor mode, where the dominant flowfield fluctuations are a time-varying crossflow velocity. For the non-reacting jets, the nominal jet-to-crossflow momentum flux ratio is 19. For the reacting jets, the nominal jet-to-crossflow momentum flux ratio is 6. Two cross sectional planes parallel to the jet injection wall are investigated: 1 and 2.7 jet diameters from the jet injection wall. The combustor crossflow high frequency wall mounted pressure data is given for each test case. The velocity and OH-PLIF data is presented as instantaneous snapshots, time and phase averaged flowfields, modal decompositions using Proper Orthogonal Decomposition and Dynamic Mode Decomposition, and a jet cycle analysis relative to the crossflow acoustic cycle. Analysis of the five test cases shows that the jet cross sectional velocity and OH-PLIF dynamics display a multitude of dynamics. These are often organized into shear layer dynamics and wake dynamics, but are not mutually exclusive. For large unsteady crossflow velocity oscillations at the 1st axial combustor mode, both dynamics show strong organization at the unsteady crossflow frequency. Deciphering these dynamics is complicated by the fact that the ostensible jet response to the time-varying crossflow is a time-varying jet penetration. This drives the jet toward and away from the jet injection wall. These motions are perpendicular to the laser sheet and creates significant out-of-plane motions. The amplitude of crossflow unsteadiness appears to play a role in the sharpness of the wake dynamics. For the non-reacting cases, the wake dynamics are strong and dominant spectral features in the flowfield. For the reacting cases, the wake dynamics are spectrally distinct in the lower amplitude crossflow unsteadiness case, but a large unsteady amplitude crossflow appears to suppress the spectral bands in the frequency range corresponding to wake vortex dynamics.

Fundamental Insights into Combustion Instability Predictions in Aerospace Propulsion

NASA Astrophysics Data System (ADS)

Huang, Cheng

Integrated multi-fidelity modeling has been performed for combustion instability in aerospace propulsion, which includes two levels of analysis: first, computational fluid dynamics (CFD) using hybrid RANS/LES simulations for underlying physics investigations (high-fidelity modeling); second, modal decomposition techniques for diagnostics (analysis & validation); third, development of flame response model using model reduction techniques for practical design applications (low-order model). For the high-fidelity modeling, the relevant CFD code development work is moving towards combustion instability prediction for liquid propulsion system. A laboratory-scale single-element lean direct injection (LDI) gas turbine combustor is used for configuration that produces self-excited combustion instability. The model gas turbine combustor is featured with an air inlet section, air plenum, swirler-venturi-injector assembly, combustion chamber, and exit nozzle. The combustor uses liquid fuel (Jet-A/FT-SPK) and heated air up to 800K. Combustion dynamics investigations are performed with the same geometry and operating conditions concurrently between the experiment and computation at both high (φ=0.6) and low (φ=0.36) equivalence ratios. The simulation is able to reach reasonable agreement with experiment measurements in terms of the pressure signal. Computational analyses are also performed using an acoustically-open geometry to investigate the characteristic hydrodynamics in the combustor with both constant and perturbed inlet mass flow rates. Two hydrodynamic modes are identified by using Dynamic Mode Decomposition (DMD) analysis: Vortex Breakdown Bubble (VBB) and swirling modes. Following that, the closed geometry simulation results are analyzed in three steps. In step one, a detailed cycle analysis shows two physically important couplings in the combustor: first, the acoustic compression enhances the spray drop breakup and vaporization, and generates more gaseous fuel for reaction; second, the acoustic compression couples with the unsteady hydrodynamics found in the open-geometry simulation, enhances the fuel/air mixing, and triggers a large amount of heat addition. In step two, a modal analysis using DMD extracts the dynamic features of important modes in the combustor, and identifies the presence of Precessing Vortex Core (PVC) mode and its nonlinear interactions with acoustic modes. Moreover, the DMD analysis helps to establish the couplings between the hydrodynamics and acoustics in terms of frequencies. In step 3, Rayleigh index analysis provides a quantitative assessment of acoustics/combustion couplings and identifies local regions for instability driving/damping. Two modal decomposition techniques, Proper Orthogonal Decomposition (POD) and Dynamic Mode Decomposition (DMD), are assessed in terms of their capabilities in extracting important information from the original simulation dataset and in validating the computational results using the experiment measurement. A POD analysis provides a series of modes with decreasing energy content and it offers an efficient and optimized way to represent a large dataset. The frequency-based DMD technique provides modes that correspond to all single frequencies. For the low-order modeling, fundamental aspects are examined to study necessary conditions, criteria and approaches to develop a reduced-order model (ROM) that is able to represent generic combustion/flame responses, which then can be used in an engineering level tool to provide efficient predictions of combustion instability for practical design applications. Explorations are focused on model reduction techniques by using the so-called POD/Galerkin method. The method uses the numerical solutions of the model equations as the database for building a set of POD eigen-bases. Specifically, the numerical solutions are calculated by perturbing quantities of interest such as the inlet conditions. The POD-derived eigen-bases are, in turn, used in conjunction with a Galerkin procedure to reduce the governing partial differential equation to an ordinary differential equation, which constitutes the ROM. Once the ROM is established, it can then be used as a lower-order test-bed to predict detailed results within certain parametric ranges at a fraction of the cost of solving the full governing equations. A detailed assessment is performed on the method in two parts. In part one, a one-dimensional scalar reaction-advection model equation is used for fundamental investigations, which include verification of the POD eigen-basis calculation and of the ROM development procedure. Moreover, certain criteria during ROM development are established: 1. a necessary number of POD modes that should be included to guarantee a stable ROM; 2. the need for the numerical discretization scheme to be consistent between the original CFD and the developed ROM. Furthermore, the predictive capabilities of the resulting ROM are evaluated to test its limits and to validate the values of applying broadband forcing in improving the ROM performance. In part two, the exploration is extended to a vector system of equations. Using the one-dimensional Euler equation is used as a model equation. A numerical stability issue is identified during the ROM development, the cause of which is further studied and attributed to the normalization methods implemented to generate coupled POD eigen-bases for vector variables. (Abstract shortened by UMI.).

Characterization of the low-frequency unsteadines in LES data of supersonic and hypersonic STBLI

NASA Astrophysics Data System (ADS)

Helm, Clara; Martin, Pino

2016-11-01

In a recent study, Priebe et al. (JFM 2016) used Dynamic Mode Decomposition (DMD) to analyze DNS data of a Mach 3 ramp-generated shock and turbulent boundary layer interaction (STBLI). The authors found that the reconstructed low-frequency DMD modes took on the form of Görtler-like vortices downstream of separation. The five reconstructed modes reproduced the low-frequency dynamics of the separation bubble accurately. Martín et al. (AIAA2016-3341) and Martín et al. (APS, DFD 2016) show that the low-frequency unsteadiness in STBLI results from an inviscid centrifugal instability similar to that found in separated subsonic and laminar flows, and that the turbulence is modulated but passive to the global mode. In this work we further characterize the Görtler-like vortices using LES data of Mach 3 and Mach 7 separated STBLIs. We find that the Görtler-like vortices are unsteady, and we quantify the wavelength, amplitude and the aperiodic development of these structures. This work is supported by the Air Force Office of Scientific Research under Grant AF9550-15-1-0284.

A Molecular Dynamic Modeling of Hemoglobin-Hemoglobin Interactions

NASA Astrophysics Data System (ADS)

Wu, Tao; Yang, Ye; Sheldon Wang, X.; Cohen, Barry; Ge, Hongya

2010-05-01

In this paper, we present a study of hemoglobin-hemoglobin interaction with model reduction methods. We begin with a simple spring-mass system with given parameters (mass and stiffness). With this known system, we compare the mode superposition method with Singular Value Decomposition (SVD) based Principal Component Analysis (PCA). Through PCA we are able to recover the principal direction of this system, namely the model direction. This model direction will be matched with the eigenvector derived from mode superposition analysis. The same technique will be implemented in a much more complicated hemoglobin-hemoglobin molecule interaction model, in which thousands of atoms in hemoglobin molecules are coupled with tens of thousands of T3 water molecule models. In this model, complex inter-atomic and inter-molecular potentials are replaced by nonlinear springs. We employ the same method to get the most significant modes and their frequencies of this complex dynamical system. More complex physical phenomena can then be further studied by these coarse grained models.

Interacting Multiscale Acoustic Vortices as Coherent Excitations in Dust Acoustic Wave Turbulence

NASA Astrophysics Data System (ADS)

Lin, Po-Cheng; I, Lin

2018-03-01

In this work, using three-dimensional intermittent dust acoustic wave turbulence in a dusty plasma as a platform and multidimensional empirical mode decomposition into different-scale modes in the 2 +1 D spatiotemporal space, we demonstrate the experimental observation of the interacting multiscale acoustic vortices, winding around wormlike amplitude hole filaments coinciding with defect filaments, as the basic coherent excitations for acoustic-type wave turbulence. For different decomposed modes, the self-similar rescaled stretched exponential lifetime histograms of amplitude hole filaments, and the self-similar power spectra of dust density fluctuations, indicate that similar dynamical rules are followed over a wide range of scales. In addition to the intermode acoustic vortex pair generation, propagation, or annihilation, the intra- and intermode interactions of acoustic vortices with the same or opposite helicity, their entanglement and synchronization, are found to be the key dynamical processes in acoustic wave turbulence, akin to the interacting multiscale vortices around wormlike cores observed in hydrodynamic turbulence.

Topics in Modeling of Cochlear Dynamics: Computation, Response and Stability Analysis

NASA Astrophysics Data System (ADS)

Filo, Maurice G.

This thesis touches upon several topics in cochlear modeling. Throughout the literature, mathematical models of the cochlea vary according to the degree of biological realism to be incorporated. This thesis casts the cochlear model as a continuous space-time dynamical system using operator language. This framework encompasses a wider class of cochlear models and makes the dynamics more transparent and easier to analyze before applying any numerical method to discretize space. In fact, several numerical methods are investigated to study the computational efficiency of the finite dimensional realizations in space. Furthermore, we study the effects of the active gain perturbations on the stability of the linearized dynamics. The stability analysis is used to explain possible mechanisms underlying spontaneous otoacoustic emissions and tinnitus. Dynamic Mode Decomposition (DMD) is introduced as a useful tool to analyze the response of nonlinear cochlear models. Cochlear response features are illustrated using DMD which has the advantage of explicitly revealing the spatial modes of vibrations occurring in the Basilar Membrane (BM). Finally, we address the dynamic estimation problem of BM vibrations using Extended Kalman Filters (EKF). Due to the limitations of noninvasive sensing schemes, such algorithms are inevitable to estimate the dynamic behavior of a living cochlea.

Adaptive Filtration of Physiological Artifacts in EEG Signals in Humans Using Empirical Mode Decomposition

NASA Astrophysics Data System (ADS)

Grubov, V. V.; Runnova, A. E.; Hramov, A. E.

2018-05-01

A new method for adaptive filtration of experimental EEG signals in humans and for removal of different physiological artifacts has been proposed. The algorithm of the method includes empirical mode decomposition of EEG, determination of the number of empirical modes that are considered, analysis of the empirical modes and search for modes that contains artifacts, removal of these modes, and reconstruction of the EEG signal. The method was tested on experimental human EEG signals and demonstrated high efficiency in the removal of different types of physiological EEG artifacts.

An Eigensystem Realization Algorithm (ERA) for modal parameter identification and model reduction

NASA Technical Reports Server (NTRS)

Juang, J. N.; Pappa, R. S.

1985-01-01

A method, called the Eigensystem Realization Algorithm (ERA), is developed for modal parameter identification and model reduction of dynamic systems from test data. A new approach is introduced in conjunction with the singular value decomposition technique to derive the basic formulation of minimum order realization which is an extended version of the Ho-Kalman algorithm. The basic formulation is then transformed into modal space for modal parameter identification. Two accuracy indicators are developed to quantitatively identify the system modes and noise modes. For illustration of the algorithm, examples are shown using simulation data and experimental data for a rectangular grid structure.

Lamb Waves Decomposition and Mode Identification Using Matching Pursuit Method

DTIC Science & Technology

2009-01-01

Wigner - Ville distribution ( WVD ). However, WVD suffers from severe interferences, called cross-terms. Cross- terms are the area of a time-frequency...transform (STFT), wavelet transform, Wigner - Ville distribution , matching pursuit decomposition, etc. 1 Report Documentation Page Form ApprovedOMB No...MP decomposition using chirplet dictionary was applied to a simulated S0 mode Lamb wave shown previously in Figure 2a. Wigner - Ville distribution of

Application of empirical mode decomposition in removing fidgeting interference in doppler radar life signs monitoring devices.

PubMed

Mostafanezhad, Isar; Boric-Lubecke, Olga; Lubecke, Victor; Mandic, Danilo P

2009-01-01

Empirical Mode Decomposition has been shown effective in the analysis of non-stationary and non-linear signals. As an application in wireless life signs monitoring in this paper we use this method in conditioning the signals obtained from the Doppler device. Random physical movements, fidgeting, of the human subject during a measurement can fall on the same frequency of the heart or respiration rate and interfere with the measurement. It will be shown how Empirical Mode Decomposition can break the radar signal down into its components and help separate and remove the fidgeting interference.

Nonlinear Dynamical Modes as a Basis for Short-Term Forecast of Climate Variability

NASA Astrophysics Data System (ADS)

Feigin, A. M.; Mukhin, D.; Gavrilov, A.; Seleznev, A.; Loskutov, E.

2017-12-01

We study abilities of data-driven stochastic models constructed by nonlinear dynamical decomposition of spatially distributed data to quantitative (short-term) forecast of climate characteristics. We compare two data processing techniques: (i) widely used empirical orthogonal function approach, and (ii) nonlinear dynamical modes (NDMs) framework [1,2]. We also make comparison of two kinds of the prognostic models: (i) traditional autoregression (linear) model and (ii) model in the form of random ("stochastic") nonlinear dynamical system [3]. We apply all combinations of the above-mentioned data mining techniques and kinds of models to short-term forecasts of climate indices based on sea surface temperature (SST) data. We use NOAA_ERSST_V4 dataset (monthly SST with space resolution 20 × 20) covering the tropical belt and starting from the year 1960. We demonstrate that NDM-based nonlinear model shows better prediction skill versus EOF-based linear and nonlinear models. Finally we discuss capability of NDM-based nonlinear model for long-term (decadal) prediction of climate variability. [1] D. Mukhin, A. Gavrilov, E. Loskutov , A.Feigin, J.Kurths, 2015: Principal nonlinear dynamical modes of climate variability, Scientific Reports, rep. 5, 15510; doi: 10.1038/srep15510. [2] Gavrilov, A., Mukhin, D., Loskutov, E., Volodin, E., Feigin, A., & Kurths, J., 2016: Method for reconstructing nonlinear modes with adaptive structure from multidimensional data. Chaos: An Interdisciplinary Journal of Nonlinear Science, 26(12), 123101. [3] Ya. Molkov, D. Mukhin, E. Loskutov, A. Feigin, 2012: Random dynamical models from time series. Phys. Rev. E, Vol. 85, n.3.

Lumley's PODT definition of large eddies and a trio of numerical procedures. [Proper Orthogonal Decomposition Theorem

NASA Technical Reports Server (NTRS)

Payne, Fred R.

1992-01-01

Lumley's 1967 Moscow paper provided, for the first time, a completely rational definition of the physically-useful term 'large eddy', popular for a half-century. The numerical procedures based upon his results are: (1) PODT (Proper Orthogonal Decomposition Theorem), which extracts the Large Eddy structure of stochastic processes from physical or computer simulation two-point covariances, and 2) LEIM (Large-Eddy Interaction Model), a predictive scheme for the dynamical large eddies based upon higher order turbulence modeling. Earlier Lumley's work (1964) forms the basis for the final member of the triad of numerical procedures: this predicts the global neutral modes of turbulence which have surprising agreement with both structural eigenmodes and those obtained from the dynamical equations. The ultimate goal of improved engineering design tools for turbulence may be near at hand, partly due to the power and storage of 'supermicrocomputer' workstations finally becoming adequate for the demanding numerics of these procedures.

Nearside-farside, local angular momentum and resummation theories: Useful tools for understanding the dynamics of complex-mode reactions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hankel, Marlies, E-mail: m.hankel@uq.edu.au, E-mail: j.n.l.connor@manchester.ac.uk; Connor, J. N. L., E-mail: m.hankel@uq.edu.au, E-mail: j.n.l.connor@manchester.ac.uk

2015-07-15

A valuable tool for understanding the dynamics of direct reactions is Nearside-Farside (NF) scattering theory. It makes a decomposition of the (resummed) partial wave series for the scattering amplitude, both for the differential cross section (DCS) and the Local Angular Momentum (LAM). This paper makes the first combined application of these techniques to complex-mode reactions. We ask if NF theory is a useful tool for their identification, in particular, can it distinguish complex-mode from direct-mode reactions? We also ask whether NF theory can identify NF interference oscillations in the full DCSs of complex-mode reactions. Our investigation exploits the fact thatmore » accurate quantum scattering matrix elements have recently become available for complex-mode reactions. We first apply NF theory to two simple models for the scattering amplitude of a complex-mode reaction: One involves a single Legendre polynomial; the other involves a single Legendre function of the first kind, whose form is suggested by complex angular momentum theory. We then study, at fixed translational energies, four state-to-state complex-mode reactions. They are: S({sup 1}D) + HD → SH + D, S({sup 1}D) + DH → SD + H, N({sup 2}D) +H{sub 2} → NH + H, and H{sup +} + D{sub 2} → HD + D{sup +}. We compare the NF results for the DCSs and LAMs with those for a state-to-state direct reaction, namely, F + H{sub 2} → FH + H. We demonstrate that NF theory is a valuable tool for identifying and analyzing the dynamics of complex-mode reactions.« less

A data-driven method to enhance vibration signal decomposition for rolling bearing fault analysis

NASA Astrophysics Data System (ADS)

Grasso, M.; Chatterton, S.; Pennacchi, P.; Colosimo, B. M.

2016-12-01

Health condition analysis and diagnostics of rotating machinery requires the capability of properly characterizing the information content of sensor signals in order to detect and identify possible fault features. Time-frequency analysis plays a fundamental role, as it allows determining both the existence and the causes of a fault. The separation of components belonging to different time-frequency scales, either associated to healthy or faulty conditions, represents a challenge that motivates the development of effective methodologies for multi-scale signal decomposition. In this framework, the Empirical Mode Decomposition (EMD) is a flexible tool, thanks to its data-driven and adaptive nature. However, the EMD usually yields an over-decomposition of the original signals into a large number of intrinsic mode functions (IMFs). The selection of most relevant IMFs is a challenging task, and the reference literature lacks automated methods to achieve a synthetic decomposition into few physically meaningful modes by avoiding the generation of spurious or meaningless modes. The paper proposes a novel automated approach aimed at generating a decomposition into a minimal number of relevant modes, called Combined Mode Functions (CMFs), each consisting in a sum of adjacent IMFs that share similar properties. The final number of CMFs is selected in a fully data driven way, leading to an enhanced characterization of the signal content without any information loss. A novel criterion to assess the dissimilarity between adjacent CMFs is proposed, based on probability density functions of frequency spectra. The method is suitable to analyze vibration signals that may be periodically acquired within the operating life of rotating machineries. A rolling element bearing fault analysis based on experimental data is presented to demonstrate the performances of the method and the provided benefits.

Lumley decomposition of turbulent boundary layer at high Reynolds numbers

NASA Astrophysics Data System (ADS)

Tutkun, Murat; George, William K.

2017-02-01

The decomposition proposed by Lumley in 1966 is applied to a high Reynolds number turbulent boundary layer. The experimental database was created by a hot-wire rake of 143 probes in the Laboratoire de Mécanique de Lille wind tunnel. The Reynolds numbers based on momentum thickness (Reθ) are 9800 and 19 100. Three-dimensional decomposition is performed, namely, proper orthogonal decomposition (POD) in the inhomogeneous and bounded wall-normal direction, Fourier decomposition in the homogeneous spanwise direction, and Fourier decomposition in time. The first POD modes in both cases carry nearly 50% of turbulence kinetic energy when the energy is integrated over Fourier dimensions. The eigenspectra always peak near zero frequency and most of the large scale, energy carrying features are found at the low end of the spectra. The spanwise Fourier mode which has the largest amount of energy is the first spanwise mode and its symmetrical pair. Pre-multiplied eigenspectra have only one distinct peak and it matches the secondary peak observed in the log-layer of pre-multiplied velocity spectra. Energy carrying modes obtained from the POD scale with outer scaling parameters. Full or partial reconstruction of turbulent velocity signal based only on energetic modes or non-energetic modes revealed the behaviour of urms in distinct regions across the boundary layer. When urms is based on energetic reconstruction, there exists (a) an exponential decay from near wall to log-layer, (b) a constant layer through the log-layer, and (c) another exponential decay in the outer region. The non-energetic reconstruction reveals that urms has (a) an exponential decay from the near-wall to the end of log-layer and (b) a constant layer in the outer region. Scaling of urms using the outer parameters is best when both energetic and non-energetic profiles are combined.

System and methods for determining masking signals for applying empirical mode decomposition (EMD) and for demodulating intrinsic mode functions obtained from application of EMD

DOEpatents

Senroy, Nilanjan [New Delhi, IN; Suryanarayanan, Siddharth [Littleton, CO

2011-03-15

A computer-implemented method of signal processing is provided. The method includes generating one or more masking signals based upon a computed Fourier transform of a received signal. The method further includes determining one or more intrinsic mode functions (IMFs) of the received signal by performing a masking-signal-based empirical mode decomposition (EMD) using the at least one masking signal.

New Insights on Insect's Silent Flight. Part I: Vortex Dynamics and Wing Morphing

NASA Astrophysics Data System (ADS)

Ren, Yan; Liu, Geng; Dong, Haibo; Geng, Biao; Zheng, Xudong; Xue, Qian

2016-11-01

Insects are capable of conducting silent flights. This is attributed to its specially designed wing material properties for the control of vibration and surface morphing during the flapping flight. In current work, we focus on the roles of dynamic wing morphing on the unsteady vortex dynamics of a cicada in steady flight. A 3D image-based surface reconstruction method is used to obtain kinematical and morphological data of cicada wings from high-quality high-speed videos. The observed morphing wing kinematics is highly complex and a singular value decomposition method is used to decompose the wing motion to several dominant modes with distinct motion features. A high-fidelity immersed-boundary-based flow solver is then used to study the vortex dynamics in details. The results show that vortical structures closely relate to the morphing mode, which plays key role in the development and attachment of leading-edge vortex (LEV), thus helps the silent flapping of the cicada wings. This work is supported by AFOSR FA9550-12-1-0071 and NSF CBET-1313217.

Kerr-like behaviour of second harmonic generation in the far-off resonant regime

NASA Astrophysics Data System (ADS)

Peřinová, Vlasta; Lukš, Antonín; Křepelka, Jaromír; Leoński, Wiesław; Peřina, Jan

2018-05-01

We separate the Kerr-like behaviour of the second-harmonic generation in the far-off resonant regime from the oscillations caused by the time-dependence of the interaction energy. To this purpose, we consider the approximation obtained from the exact dynamics by the method of small rotations. The Floquet-type decomposition of the approximate dynamics comprises the Kerr-like dynamics and oscillations of the same order of magnitude as those assumed for the exact dynamics of the second-harmonic generation. We have found that a superposition of two states of concentrated quantum phase arises in the fundamental mode in the second-harmonic generation in the far-off resonant limit at a later time than a superposition of two coherent states in the corresponding Kerr medium and the difference is larger for higher initial coherent amplitudes. The quantum phase fluctuation is higher for the same initial coherent amplitudes in the fundamental mode in the second-harmonic generation in the far-off resonant limit than in the corresponding Kerr medium and the difference is larger for higher initial coherent amplitudes.

Fast modal decomposition for optical fibers using digital holography.

PubMed

Lyu, Meng; Lin, Zhiquan; Li, Guowei; Situ, Guohai

2017-07-26

Eigenmode decomposition of the light field at the output end of optical fibers can provide fundamental insights into the nature of electromagnetic-wave propagation through the fibers. Here we present a fast and complete modal decomposition technique for step-index optical fibers. The proposed technique employs digital holography to measure the light field at the output end of the multimode optical fiber, and utilizes the modal orthonormal property of the basis modes to calculate the modal coefficients of each mode. Optical experiments were carried out to demonstrate the proposed decomposition technique, showing that this approach is fast, accurate and cost-effective.

Data-adaptive harmonic spectra and multilayer Stuart-Landau models

NASA Astrophysics Data System (ADS)

Chekroun, Mickaël D.; Kondrashov, Dmitri

2017-09-01

Harmonic decompositions of multivariate time series are considered for which we adopt an integral operator approach with periodic semigroup kernels. Spectral decomposition theorems are derived that cover the important cases of two-time statistics drawn from a mixing invariant measure. The corresponding eigenvalues can be grouped per Fourier frequency and are actually given, at each frequency, as the singular values of a cross-spectral matrix depending on the data. These eigenvalues obey, furthermore, a variational principle that allows us to define naturally a multidimensional power spectrum. The eigenmodes, as far as they are concerned, exhibit a data-adaptive character manifested in their phase which allows us in turn to define a multidimensional phase spectrum. The resulting data-adaptive harmonic (DAH) modes allow for reducing the data-driven modeling effort to elemental models stacked per frequency, only coupled at different frequencies by the same noise realization. In particular, the DAH decomposition extracts time-dependent coefficients stacked by Fourier frequency which can be efficiently modeled—provided the decay of temporal correlations is sufficiently well-resolved—within a class of multilayer stochastic models (MSMs) tailored here on stochastic Stuart-Landau oscillators. Applications to the Lorenz 96 model and to a stochastic heat equation driven by a space-time white noise are considered. In both cases, the DAH decomposition allows for an extraction of spatio-temporal modes revealing key features of the dynamics in the embedded phase space. The multilayer Stuart-Landau models (MSLMs) are shown to successfully model the typical patterns of the corresponding time-evolving fields, as well as their statistics of occurrence.

Multi-focus image fusion based on window empirical mode decomposition

NASA Astrophysics Data System (ADS)

Qin, Xinqiang; Zheng, Jiaoyue; Hu, Gang; Wang, Jiao

2017-09-01

In order to improve multi-focus image fusion quality, a novel fusion algorithm based on window empirical mode decomposition (WEMD) is proposed. This WEMD is an improved form of bidimensional empirical mode decomposition (BEMD), due to its decomposition process using the adding window principle, effectively resolving the signal concealment problem. We used WEMD for multi-focus image fusion, and formulated different fusion rules for bidimensional intrinsic mode function (BIMF) components and the residue component. For fusion of the BIMF components, the concept of the Sum-modified-Laplacian was used and a scheme based on the visual feature contrast adopted; when choosing the residue coefficients, a pixel value based on the local visibility was selected. We carried out four groups of multi-focus image fusion experiments and compared objective evaluation criteria with other three fusion methods. The experimental results show that the proposed fusion approach is effective and performs better at fusing multi-focus images than some traditional methods.

Studying Climate Response to Forcing by the Nonlinear Dynamical Mode Decomposition

NASA Astrophysics Data System (ADS)

Mukhin, Dmitry; Gavrilov, Andrey; Loskutov, Evgeny; Feigin, Alexander

2017-04-01

An analysis of global climate response to external forcing, both anthropogenic (mainly, CO2 and aerosol) and natural (solar and volcanic), is needed for adequate predictions of global climate change. Being complex dynamical system, the climate reacts to external perturbations exciting feedbacks (both positive and negative) making the response non-trivial and poorly predictable. Thus an extraction of internal modes of climate system, investigation of their interaction with external forcings and further modeling and forecast of their dynamics, are all the problems providing the success of climate modeling. In the report the new method for principal mode extraction from climate data is presented. The method is based on the Nonlinear Dynamical Mode (NDM) expansion [1,2], but takes into account a number of external forcings applied to the system. Each NDM is represented by hidden time series governing the observed variability, which, together with external forcing time series, are mapped onto data space. While forcing time series are considered to be known, the hidden unknown signals underlying the internal climate dynamics are extracted from observed data by the suggested method. In particular, it gives us an opportunity to study the evolution of principal system's mode structure in changing external conditions and separate the internal climate variability from trends forced by external perturbations. Furthermore, the modes so obtained can be extrapolated beyond the observational time series, and long-term prognosis of modes' structure including characteristics of interconnections and responses to external perturbations, can be carried out. In this work the method is used for reconstructing and studying the principal modes of climate variability on inter-annual and decadal time scales accounting the external forcings such as anthropogenic emissions, variations of the solar activity and volcanic activity. The structure of the obtained modes as well as their response to external factors, e.g. forecast their change in 21 century under different CO2 emission scenarios, are discussed. [1] Mukhin, D., Gavrilov, A., Feigin, A., Loskutov, E., & Kurths, J. (2015). Principal nonlinear dynamical modes of climate variability. Scientific Reports, 5, 15510. http://doi.org/10.1038/srep15510 [2] Gavrilov, A., Mukhin, D., Loskutov, E., Volodin, E., Feigin, A., & Kurths, J. (2016). Method for reconstructing nonlinear modes with adaptive structure from multidimensional data. Chaos: An Interdisciplinary Journal of Nonlinear Science, 26(12), 123101. http://doi.org/10.1063/1.4968852

Experimental validation of a structural damage detection method based on marginal Hilbert spectrum

NASA Astrophysics Data System (ADS)

Banerji, Srishti; Roy, Timir B.; Sabamehr, Ardalan; Bagchi, Ashutosh

2017-04-01

Structural Health Monitoring (SHM) using dynamic characteristics of structures is crucial for early damage detection. Damage detection can be performed by capturing and assessing structural responses. Instrumented structures are monitored by analyzing the responses recorded by deployed sensors in the form of signals. Signal processing is an important tool for the processing of the collected data to diagnose anomalies in structural behavior. The vibration signature of the structure varies with damage. In order to attain effective damage detection, preservation of non-linear and non-stationary features of real structural responses is important. Decomposition of the signals into Intrinsic Mode Functions (IMF) by Empirical Mode Decomposition (EMD) and application of Hilbert-Huang Transform (HHT) addresses the time-varying instantaneous properties of the structural response. The energy distribution among different vibration modes of the intact and damaged structure depicted by Marginal Hilbert Spectrum (MHS) detects location and severity of the damage. The present work investigates damage detection analytically and experimentally by employing MHS. The testing of this methodology for different damage scenarios of a frame structure resulted in its accurate damage identification. The sensitivity of Hilbert Spectral Analysis (HSA) is assessed with varying frequencies and damage locations by means of calculating Damage Indices (DI) from the Hilbert spectrum curves of the undamaged and damaged structures.

Instantaneous Respiratory Estimation from Thoracic Impedance by Empirical Mode Decomposition.

PubMed

Wang, Fu-Tai; Chan, Hsiao-Lung; Wang, Chun-Li; Jian, Hung-Ming; Lin, Sheng-Hsiung

2015-07-07

Impedance plethysmography provides a way to measure respiratory activity by sensing the change of thoracic impedance caused by inspiration and expiration. This measurement imposes little pressure on the body and uses the human body as the sensor, thereby reducing the need for adjustments as body position changes and making it suitable for long-term or ambulatory monitoring. The empirical mode decomposition (EMD) can decompose a signal into several intrinsic mode functions (IMFs) that disclose nonstationary components as well as stationary components and, similarly, capture respiratory episodes from thoracic impedance. However, upper-body movements usually produce motion artifacts that are not easily removed by digital filtering. Moreover, large motion artifacts disable the EMD to decompose respiratory components. In this paper, motion artifacts are detected and replaced by the data mirrored from the prior and the posterior before EMD processing. A novel intrinsic respiratory reconstruction index that considers both global and local properties of IMFs is proposed to define respiration-related IMFs for respiration reconstruction and instantaneous respiratory estimation. Based on the experiments performing a series of static and dynamic physical activates, our results showed the proposed method had higher cross correlations between respiratory frequencies estimated from thoracic impedance and those from oronasal airflow based on small window size compared to the Fourier transform-based method.

Instantaneous Respiratory Estimation from Thoracic Impedance by Empirical Mode Decomposition

PubMed Central

Wang, Fu-Tai; Chan, Hsiao-Lung; Wang, Chun-Li; Jian, Hung-Ming; Lin, Sheng-Hsiung

2015-01-01

Impedance plethysmography provides a way to measure respiratory activity by sensing the change of thoracic impedance caused by inspiration and expiration. This measurement imposes little pressure on the body and uses the human body as the sensor, thereby reducing the need for adjustments as body position changes and making it suitable for long-term or ambulatory monitoring. The empirical mode decomposition (EMD) can decompose a signal into several intrinsic mode functions (IMFs) that disclose nonstationary components as well as stationary components and, similarly, capture respiratory episodes from thoracic impedance. However, upper-body movements usually produce motion artifacts that are not easily removed by digital filtering. Moreover, large motion artifacts disable the EMD to decompose respiratory components. In this paper, motion artifacts are detected and replaced by the data mirrored from the prior and the posterior before EMD processing. A novel intrinsic respiratory reconstruction index that considers both global and local properties of IMFs is proposed to define respiration-related IMFs for respiration reconstruction and instantaneous respiratory estimation. Based on the experiments performing a series of static and dynamic physical activates, our results showed the proposed method had higher cross correlations between respiratory frequencies estimated from thoracic impedance and those from oronasal airflow based on small window size compared to the Fourier transform-based method. PMID:26198231

An optimized time varying filtering based empirical mode decomposition method with grey wolf optimizer for machinery fault diagnosis

NASA Astrophysics Data System (ADS)

Zhang, Xin; Liu, Zhiwen; Miao, Qiang; Wang, Lei

2018-03-01

A time varying filtering based empirical mode decomposition (EMD) (TVF-EMD) method was proposed recently to solve the mode mixing problem of EMD method. Compared with the classical EMD, TVF-EMD was proven to improve the frequency separation performance and be robust to noise interference. However, the decomposition parameters (i.e., bandwidth threshold and B-spline order) significantly affect the decomposition results of this method. In original TVF-EMD method, the parameter values are assigned in advance, which makes it difficult to achieve satisfactory analysis results. To solve this problem, this paper develops an optimized TVF-EMD method based on grey wolf optimizer (GWO) algorithm for fault diagnosis of rotating machinery. Firstly, a measurement index termed weighted kurtosis index is constructed by using kurtosis index and correlation coefficient. Subsequently, the optimal TVF-EMD parameters that match with the input signal can be obtained by GWO algorithm using the maximum weighted kurtosis index as objective function. Finally, fault features can be extracted by analyzing the sensitive intrinsic mode function (IMF) owning the maximum weighted kurtosis index. Simulations and comparisons highlight the performance of TVF-EMD method for signal decomposition, and meanwhile verify the fact that bandwidth threshold and B-spline order are critical to the decomposition results. Two case studies on rotating machinery fault diagnosis demonstrate the effectiveness and advantages of the proposed method.

Linearised dynamics and non-modal instability analysis of an impinging under-expanded supersonic jet

NASA Astrophysics Data System (ADS)

Karami, Shahram; Stegeman, Paul C.; Theofilis, Vassilis; Schmid, Peter J.; Soria, Julio

2018-04-01

Non-modal instability analysis of the shear layer near the nozzle of a supersonic under-expanded impinging jet is studied. The shear layer instability is considered to be one of the main components of the feedback loop in supersonic jets. The feedback loop is observed in instantaneous visualisations of the density field where it is noted that acoustic waves scattered by the nozzle lip internalise as shear layer instabilities. A modal analysis describes the asymptotic limit of the instability disturbances and fails to capture short-time responses. Therefore, a non-modal analysis which allows the quantitative description of the short-time amplification or decay of a disturbance is performed by means of a local far-field pressure pulse. An impulse response analysis is performed which allows a wide range of frequencies to be excited. The temporal and spatial growths of the disturbances in the shear layer near the nozzle are studied by decomposing the response using dynamic mode decomposition and Hilbert transform analysis. The short-time response shows that disturbances with non-dimensionalised temporal frequencies in the range of 1 to 4 have positive growth rates in the shear layer. The Hilbert transform analysis shows that high non-dimensionalised temporal frequencies (>4) are dampened immediately, whereas low non-dimensionalised temporal frequencies (<1) are neutral. Both dynamic mode decomposition and Hilbert transform analysis show that spatial frequencies between 1 and 3 have positive spatial growth rates. Finally, the envelope of the streamwise velocity disturbances reveals the presence of a convective instability.

Data-driven Inference and Investigation of Thermosphere Dynamics and Variations

NASA Astrophysics Data System (ADS)

Mehta, P. M.; Linares, R.

2017-12-01

This paper presents a methodology for data-driven inference and investigation of thermosphere dynamics and variations. The approach uses data-driven modal analysis to extract the most energetic modes of variations for neutral thermospheric species using proper orthogonal decomposition, where the time-independent modes or basis represent the dynamics and the time-depedent coefficients or amplitudes represent the model parameters. The data-driven modal analysis approach combined with sparse, discrete observations is used to infer amplitues for the dynamic modes and to calibrate the energy content of the system. In this work, two different data-types, namely the number density measurements from TIMED/GUVI and the mass density measurements from CHAMP/GRACE are simultaneously ingested for an accurate and self-consistent specification of the thermosphere. The assimilation process is achieved with a non-linear least squares solver and allows estimation/tuning of the model parameters or amplitudes rather than the driver. In this work, we use the Naval Research Lab's MSIS model to derive the most energetic modes for six different species, He, O, N2, O2, H, and N. We examine the dominant drivers of variations for helium in MSIS and observe that seasonal latitudinal variation accounts for about 80% of the dynamic energy with a strong preference of helium for the winter hemisphere. We also observe enhanced helium presence near the poles at GRACE altitudes during periods of low solar activity (Feb 2007) as previously deduced. We will also examine the storm-time response of helium derived from observations. The results are expected to be useful in tuning/calibration of the physics-based models.

Optimal Averages for Nonlinear Signal Decompositions - Another Alternative for Empirical Mode Decomposition

DTIC Science & Technology

2014-10-01

nonlinear and non-stationary signals. It aims at decomposing a signal, via an iterative sifting procedure, into several intrinsic mode functions ...stationary signals. It aims at decomposing a signal, via an iterative sifting procedure into several intrinsic mode functions (IMFs), and each of the... function , optimization. 1 Introduction It is well known that nonlinear and non-stationary signal analysis is important and difficult. His- torically

Green's function and Bloch theory for the analysis of the dynamic response of a periodically supported beam to a moving load

NASA Astrophysics Data System (ADS)

Lassoued, R.; Lecheheb, M.; Bonnet, G.

2012-08-01

This paper describes an analytical method for the wave field induced by a moving load on a periodically supported beam. The Green's function for an Euler beam without support is evaluated by using the direct integration. Afterwards, it introduces the supports into the model established by using the superposition principle which states that the response from all the sleeper points and from the external point force add up linearly to give a total response. The periodicity of the supports is described by Bloch's theorem. The homogeneous system thus obtained represents a linear differential equation which governs rail response. It is initially solved in the homogeneous case, and it admits a no null solution if its determinant is null, this permits the establishment the dispersion equation to Bloch waves and wave bands. The Bloch waves and dispersion curves contain all the physics of the dynamic problem and the wave field induced by a dynamic load applied to the system is finally obtained by decomposition into Bloch waves, similarly to the usual decomposition into dynamic modes on a finite structure. The method is applied to obtain the field induced by a load moving at constant velocity on a thin beam supported by periodic elastic supports.

Using dynamic mode decomposition to extract cyclic behavior in the stock market

NASA Astrophysics Data System (ADS)

Hua, Jia-Chen; Roy, Sukesh; McCauley, Joseph L.; Gunaratne, Gemunu H.

2016-04-01

The presence of cyclic expansions and contractions in the economy has been known for over a century. The work reported here searches for similar cyclic behavior in stock valuations. The variations are subtle and can only be extracted through analysis of price variations of a large number of stocks. Koopman mode analysis is a natural approach to establish such collective oscillatory behavior. The difficulty is that even non-cyclic and stochastic constituents of a finite data set may be interpreted as a sum of periodic motions. However, deconvolution of these irregular dynamical facets may be expected to be non-robust, i.e., to depend on specific data set. We propose an approach to differentiate robust and non-robust features in a time series; it is based on identifying robust features with reproducible Koopman modes, i.e., those that persist between distinct sub-groupings of the data. Our analysis of stock data discovered four reproducible modes, one of which has period close to the number of trading days/year. To the best of our knowledge these cycles were not reported previously. It is particularly interesting that the cyclic behaviors persisted through the great recession even though phase relationships between stocks within the modes evolved in the intervening period.

Image fusion method based on regional feature and improved bidimensional empirical mode decomposition

NASA Astrophysics Data System (ADS)

Qin, Xinqiang; Hu, Gang; Hu, Kai

2018-01-01

The decomposition of multiple source images using bidimensional empirical mode decomposition (BEMD) often produces mismatched bidimensional intrinsic mode functions, either by their number or their frequency, making image fusion difficult. A solution to this problem is proposed using a fixed number of iterations and a union operation in the sifting process. By combining the local regional features of the images, an image fusion method has been developed. First, the source images are decomposed using the proposed BEMD to produce the first intrinsic mode function (IMF) and residue component. Second, for the IMF component, a selection and weighted average strategy based on local area energy is used to obtain a high-frequency fusion component. Third, for the residue component, a selection and weighted average strategy based on local average gray difference is used to obtain a low-frequency fusion component. Finally, the fused image is obtained by applying the inverse BEMD transform. Experimental results show that the proposed algorithm provides superior performance over methods based on wavelet transform, line and column-based EMD, and complex empirical mode decomposition, both in terms of visual quality and objective evaluation criteria.

Low-dimensional representation of near-wall dynamics in shear flows, with implications to wall-models.

PubMed

Schmid, P J; Sayadi, T

2017-03-13

The dynamics of coherent structures near the wall of a turbulent boundary layer is investigated with the aim of a low-dimensional representation of its essential features. Based on a triple decomposition into mean, coherent and incoherent motion and a dynamic mode decomposition to recover statistical information about the incoherent part of the flow field, a driven linear system coupling first- and second-order moments of the coherent structures is derived and analysed. The transfer function for this system, evaluated for a wall-parallel plane, confirms a strong bias towards streamwise elongated structures, and is proposed as an 'impedance' boundary condition which replaces the bulk of the transport between the coherent velocity field and the coherent Reynolds stresses, thus acting as a wall model for large-eddy simulations (LES). It is interesting to note that the boundary condition is non-local in space and time. The extracted model is capable of reproducing the principal Reynolds stress components for the pretransitional, transitional and fully turbulent boundary layer.This article is part of the themed issue 'Toward the development of high-fidelity models of wall turbulence at large Reynolds number'. © 2017 The Author(s).

Reduced nonlinear prognostic model construction from high-dimensional data

NASA Astrophysics Data System (ADS)

Gavrilov, Andrey; Mukhin, Dmitry; Loskutov, Evgeny; Feigin, Alexander

2017-04-01

Construction of a data-driven model of evolution operator using universal approximating functions can only be statistically justified when the dimension of its phase space is small enough, especially in the case of short time series. At the same time in many applications real-measured data is high-dimensional, e.g. it is space-distributed and multivariate in climate science. Therefore it is necessary to use efficient dimensionality reduction methods which are also able to capture key dynamical properties of the system from observed data. To address this problem we present a Bayesian approach to an evolution operator construction which incorporates two key reduction steps. First, the data is decomposed into a set of certain empirical modes, such as standard empirical orthogonal functions or recently suggested nonlinear dynamical modes (NDMs) [1], and the reduced space of corresponding principal components (PCs) is obtained. Then, the model of evolution operator for PCs is constructed which maps a number of states in the past to the current state. The second step is to reduce this time-extended space in the past using appropriate decomposition methods. Such a reduction allows us to capture only the most significant spatio-temporal couplings. The functional form of the evolution operator includes separately linear, nonlinear (based on artificial neural networks) and stochastic terms. Explicit separation of the linear term from the nonlinear one allows us to more easily interpret degree of nonlinearity as well as to deal better with smooth PCs which can naturally occur in the decompositions like NDM, as they provide a time scale separation. Results of application of the proposed method to climate data are demonstrated and discussed. The study is supported by Government of Russian Federation (agreement #14.Z50.31.0033 with the Institute of Applied Physics of RAS). 1. Mukhin, D., Gavrilov, A., Feigin, A., Loskutov, E., & Kurths, J. (2015). Principal nonlinear dynamical modes of climate variability. Scientific Reports, 5, 15510. http://doi.org/10.1038/srep15510

Nonlinear cross-field coupling on the route to broadband turbulence

NASA Astrophysics Data System (ADS)

Brandt, Christian; Thakur, Saikat C.; Cui, Lang; Gosselin, Jordan J.; Negrete, Jose, Jr.; Holland, Chris; Tynan, George R.

2013-10-01

In the linear magnetized plasma device CSDX (Controlled Shear De-correlation eXperiment) drift interchange modes are studied coexisting on top of a weak turbulence driven azimuthally symmetric, radially sheared plasma flow. In helicon discharges (helicon antenna diameter 15 cm) with increasing magnetic field (B <= 0 . 24 T) the system can be driven to fully developed broadband turbulence. Fast imaging using a refractive telescope setup is applied to study the dynamics in the azimuthal-radial cross-section. The image data is supported by Langmuir probe measurements. In the present study we examine the development of nonlinear transfer as the fully developed turbulence emerges. Nonlinear cross-field coupling between eigenmodes at different radial positions is investigated using Fourier decomposition of azimuthal eigenmodes. The coupling strength between waves at different radial positions is inferred to radial profiles and cross-field transport between adjacent magnetic flux surfaces. Nonlinear effects like synchronization, phase slippages, phase pulling and periodic pulling are observed. The effects of mode coupling and the stability of modes is compared to the dynamics of a coupled chain of Kuramoto oscillators.

Detecting scaling in the period dynamics of multimodal signals: Application to Parkinsonian tremor

NASA Astrophysics Data System (ADS)

Sapir, Nir; Karasik, Roman; Havlin, Shlomo; Simon, Ely; Hausdorff, Jeffrey M.

2003-03-01

Patients with Parkinson’s disease exhibit tremor, involuntary movement of the limbs. The frequency spectrum of tremor typically has broad peaks at “harmonic” frequencies, much like that seen in other physical processes. In general, this type of harmonic structure in the frequency domain may be due to two possible mechanisms: a nonlinear oscillation or a superposition of (multiple) independent modes of oscillation. A broad peak spectrum generally indicates that a signal is semiperiodic with a fluctuating period. These fluctuations may posses intrinsic order that can be quantified using scaling analysis. We propose a method to extract the correlation (scaling) properties in the period dynamics of multimodal oscillations, in order to distinguish between a nonlinear oscillation and a superposition of individual modes of oscillation. The method is based on our finding that the information content of the temporal correlations in a fluctuating period of a single oscillator is contained in a finite frequency band in the power spectrum, allowing for decomposition of modes by bandpass filtering. Our simulations for a nonlinear oscillation show that harmonic modes possess the same scaling properties. In contrast, when the method is applied to tremor records from patients with Parkinson’s disease, the first two modes of oscillations yield different scaling patterns, suggesting that these modes may not be simple harmonics, as might be initially assumed.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kolda, Tamara Gibson

We propose two new multilinear operators for expressing the matrix compositions that are needed in the Tucker and PARAFAC (CANDECOMP) decompositions. The first operator, which we call the Tucker operator, is shorthand for performing an n-mode matrix multiplication for every mode of a given tensor and can be employed to concisely express the Tucker decomposition. The second operator, which we call the Kruskal operator, is shorthand for the sum of the outer-products of the columns of N matrices and allows a divorce from a matricized representation and a very concise expression of the PARAFAC decomposition. We explore the properties ofmore » the Tucker and Kruskal operators independently of the related decompositions. Additionally, we provide a review of the matrix and tensor operations that are frequently used in the context of tensor decompositions.« less

Three dimensional empirical mode decomposition analysis apparatus, method and article manufacture

NASA Technical Reports Server (NTRS)

Gloersen, Per (Inventor)

2004-01-01

An apparatus and method of analysis for three-dimensional (3D) physical phenomena. The physical phenomena may include any varying 3D phenomena such as time varying polar ice flows. A repesentation of the 3D phenomena is passed through a Hilbert transform to convert the data into complex form. A spatial variable is separated from the complex representation by producing a time based covariance matrix. The temporal parts of the principal components are produced by applying Singular Value Decomposition (SVD). Based on the rapidity with which the eigenvalues decay, the first 3-10 complex principal components (CPC) are selected for Empirical Mode Decomposition into intrinsic modes. The intrinsic modes produced are filtered in order to reconstruct the spatial part of the CPC. Finally, a filtered time series may be reconstructed from the first 3-10 filtered complex principal components.

Intramolecular energy transfer and mode-specific effects in unimolecular reactions of 1,2-difluoroethane

NASA Astrophysics Data System (ADS)

Raff, Lionel M.

1989-06-01

The unimolecular decomposition reactions of 1,2-difluoroethane upon mode-specific excitation to a total internal energy of 7.5 eV are investigated using classical trajectory methods and a previously formulated empirical potential-energy surface. The decomposition channels for 1,2-difluoroethane are, in order of importance, four-center HF elimination, C-C bond rupture, and hydrogen-atom dissociation. This order is found to be independent of the particular vibrational mode excited. Neither fluorine-atom nor F2 elimination reactions are ever observed even though these dissociation channels are energetically open. For four-center HF elimination, the average fraction of the total energy partitioned into internal HF motion varies between 0.115-0.181 depending upon the particular vibrational mode initially excited. The internal energy of the fluoroethylene product lies in the range 0.716-0.776. Comparison of the present results with those previously obtained for a random distribution of the initial 1,2-difluoroethane internal energy [J. Phys. Chem. 92, 5111 (1988)], shows that numerous mode-specific effects are present in these reactions in spite of the fact that intramolecular energy transfer rates for this system are 5.88-25.5 times faster than any of the unimolecular reaction rates. Mode-specific excitation always leads to a total decomposition rate significantly larger than that obtained for a random distribution of the internal energy. Excitation of different 1,2-difluoroethane vibrational modes is found to produce as much as a 51% change in the total decomposition rate. Mode-specific effects are also seen in the product energy partitioning. The rate coefficients for decomposition into the various channels are very sensitive to the particular mode excited. A comparison of the calculated mode-specific effects with the previously determined mode-to-mode energy transfer rate coefficients [J. Chem. Phys. 89, 5680 (1988)] shows that, to some extent, the presence of mode-specific chemistry is correlated with the magnitude of the energy transfer rate. However, the particular pathways for energy flow seem to be more important than the magnitude of the rate coefficients. It is suggested that the propensity for the energy to remain isolated in small subset of modes, such as the CH2F deformation modes or the rocking modes, is primarily responsible for the observation of mode-specific chemistry. The results clearly demonstrate that an intramolecular energy transfer rate that is fast relative to the unimolecular reaction rate is not a sufficient condition to ensure the absence of mode-specific chemical effects.

Quantization of Electromagnetic Fields in Cavities

NASA Technical Reports Server (NTRS)

Kakazu, Kiyotaka; Oshiro, Kazunori

1996-01-01

A quantization procedure for the electromagnetic field in a rectangular cavity with perfect conductor walls is presented, where a decomposition formula of the field plays an essential role. All vector mode functions are obtained by using the decomposition. After expanding the field in terms of the vector mode functions, we get the quantized electromagnetic Hamiltonian.

Nonlinear mode decomposition: A noise-robust, adaptive decomposition method

NASA Astrophysics Data System (ADS)

Iatsenko, Dmytro; McClintock, Peter V. E.; Stefanovska, Aneta

2015-09-01

The signals emanating from complex systems are usually composed of a mixture of different oscillations which, for a reliable analysis, should be separated from each other and from the inevitable background of noise. Here we introduce an adaptive decomposition tool—nonlinear mode decomposition (NMD)—which decomposes a given signal into a set of physically meaningful oscillations for any wave form, simultaneously removing the noise. NMD is based on the powerful combination of time-frequency analysis techniques—which, together with the adaptive choice of their parameters, make it extremely noise robust—and surrogate data tests used to identify interdependent oscillations and to distinguish deterministic from random activity. We illustrate the application of NMD to both simulated and real signals and demonstrate its qualitative and quantitative superiority over other approaches, such as (ensemble) empirical mode decomposition, Karhunen-Loève expansion, and independent component analysis. We point out that NMD is likely to be applicable and useful in many different areas of research, such as geophysics, finance, and the life sciences. The necessary matlab codes for running NMD are freely available for download.

The Distributed Diagonal Force Decomposition Method for Parallelizing Molecular Dynamics Simulations

PubMed Central

Boršnik, Urban; Miller, Benjamin T.; Brooks, Bernard R.; Janežič, Dušanka

2011-01-01

Parallelization is an effective way to reduce the computational time needed for molecular dynamics simulations. We describe a new parallelization method, the distributed-diagonal force decomposition method, with which we extend and improve the existing force decomposition methods. Our new method requires less data communication during molecular dynamics simulations than replicated data and current force decomposition methods, increasing the parallel efficiency. It also dynamically load-balances the processors' computational load throughout the simulation. The method is readily implemented in existing molecular dynamics codes and it has been incorporated into the CHARMM program, allowing its immediate use in conjunction with the many molecular dynamics simulation techniques that are already present in the program. We also present the design of the Force Decomposition Machine, a cluster of personal computers and networks that is tailored to running molecular dynamics simulations using the distributed diagonal force decomposition method. The design is expandable and provides various degrees of fault resilience. This approach is easily adaptable to computers with Graphics Processing Units because it is independent of the processor type being used. PMID:21793007

Multidimensional k-nearest neighbor model based on EEMD for financial time series forecasting

NASA Astrophysics Data System (ADS)

Zhang, Ningning; Lin, Aijing; Shang, Pengjian

2017-07-01

In this paper, we propose a new two-stage methodology that combines the ensemble empirical mode decomposition (EEMD) with multidimensional k-nearest neighbor model (MKNN) in order to forecast the closing price and high price of the stocks simultaneously. The modified algorithm of k-nearest neighbors (KNN) has an increasingly wide application in the prediction of all fields. Empirical mode decomposition (EMD) decomposes a nonlinear and non-stationary signal into a series of intrinsic mode functions (IMFs), however, it cannot reveal characteristic information of the signal with much accuracy as a result of mode mixing. So ensemble empirical mode decomposition (EEMD), an improved method of EMD, is presented to resolve the weaknesses of EMD by adding white noise to the original data. With EEMD, the components with true physical meaning can be extracted from the time series. Utilizing the advantage of EEMD and MKNN, the new proposed ensemble empirical mode decomposition combined with multidimensional k-nearest neighbor model (EEMD-MKNN) has high predictive precision for short-term forecasting. Moreover, we extend this methodology to the case of two-dimensions to forecast the closing price and high price of the four stocks (NAS, S&P500, DJI and STI stock indices) at the same time. The results indicate that the proposed EEMD-MKNN model has a higher forecast precision than EMD-KNN, KNN method and ARIMA.

Eddy Vertical Structure Observed by Deepgliders: Evidence for the Enstrophy Inertial Range Cascade in Geostrophic Turbulence

NASA Astrophysics Data System (ADS)

Eriksen, C. C.

2016-12-01

Full water column temperature and salinity profiles and estimates of average current collected with Deepgliders were used to analyze vertical structure of mesoscale features in the western North Atlantic Ocean. Fortnightly repeat surveys over a 58 km by 58 km region centered at the Bermuda Atlantic Time Series (BATS) site southeast of Bermuda were carried out for 3 and 9 months in successive years. In addition, a section from Bermuda along Line W across the Gulf Stream to the New England Continental Slope and a pair of sections from Bermuda to the Bahamas were carried out. Absolute geostrophic current estimates constructed from these measurements and projected upon flat bottom resting ocean dynamic modes for the regions indicate nearly equal kinetic energy in the barotropic mode and first baroclinic mode. An empirical orthogonal mode decomposition of dynamic mode amplitudes demonstrates strong coupling of the barotropic and first baroclinic modes, a result resembling those reported for the Polymode experiment 3 decades ago. Higher baroclinic modes are largely independent of one another. Energy in baroclinic modes varies in inverse proportion to mode number cubed, a result predicted for an enstrophy inertial range cascade of geostrophic turbulence, believed newly detected by these observations. This (mode number)-3 dependence is found at BATS and across the Gulf Stream and Sargasso Sea. On two occasions, submesoscale anticyclones were detected at BATS whose vertical structure closely resembled the second baroclinic mode. Anomalously cold and fresh water within their cores (by as much as 3.5°C and 0.5 in salinity) suggests they were of subpolar (likely Labrador Sea) origin. These provided temporary perturbations to the vertical mode number energy spectrum.

[Dynamics of microbial biomass carbon and nitrogen during foliar litter decomposition under artificial forest gap in Pinus massoniana plantation.

PubMed

Zhang, Ming Jin; Chen, Liang Hua; Zhang, Jian; Yang, Wan Qin; Liu, Hua; Li, Xun; Zhang, Yan

2016-03-01

Nowadays large areas of plantations have caused serious ecological problems such as soil degradation and biodiversity decline. Artificial tending thinning and construction of mixed forest are frequently used ways when we manage plantations. To understand the effect of this operation mode on nutrient cycle of plantation ecosystem, we detected the dynamics of microbial bio-mass carbon and nitrogen during foliar litter decomposition of Pinus massoniana and Toona ciliate in seven types of gap in different sizes (G 1 : 100 m 2 , G 2 : 225 m 2 , G 3 : 400 m 2 , G 4 : 625 m 2 , G 5 : 900 m 2 , G 6 : 1225 m 2 , G 7 : 1600 m 2 ) of 42-year-old P. massoniana plantations in a hilly area of the upper Yang-tze River. The results showed that small and medium-sized forest gaps(G 1 -G 5 ) were more advantageous for the increment of microbial biomass carbon and nitrogen in the process of foliar litter decomposition. Along with the foliar litter decomposition during the experiment (360 d), microbial biomass carbon (MBC), microbial biomass nitrogen (MBN) in P. massoniana foliar litter and MBN in T. ciliata foliar litter first increased and then decreased, and respectively reached the maxima 9.87, 0.22 and 0.80 g·kg -1 on the 180 th d. But the peak (44.40 g·kg -1 ) of MBC in T. ciliata foliar litter appeared on the 90 th d. Microbial biomass carbon and nitrogen in T. ciliate was significantly higher than that of P. massoniana during foliar litter decomposition. Microbial biomass carbon and nitrogen in foliar litter was not only significantly associated with average daily temperature and the water content of foliar litter, but also closely related to the change of the quality of litter. Therefore, in the thinning, forest gap size could be controlled in the range of from 100 to 900 m 2 to facilitate the increase of microbial biomass carbon and nitrogen in the process of foliar litter decomposition, accelerate the decomposition of foliar litter and improve soil fertility of plantations.

Low Dimensional Tools for Flow-Structure Interaction Problems: Application to Micro Air Vehicles

NASA Technical Reports Server (NTRS)

Schmit, Ryan F.; Glauser, Mark N.; Gorton, Susan A.

2003-01-01

A low dimensional tool for flow-structure interaction problems based on Proper Orthogonal Decomposition (POD) and modified Linear Stochastic Estimation (mLSE) has been proposed and was applied to a Micro Air Vehicle (MAV) wing. The method utilizes the dynamic strain measurements from the wing to estimate the POD expansion coefficients from which an estimation of the velocity in the wake can be obtained. For this experiment the MAV wing was set at five different angles of attack, from 0 deg to 20 deg. The tunnel velocities varied from 44 to 58 ft/sec with corresponding Reynolds numbers of 46,000 to 70,000. A stereo Particle Image Velocimetry (PIV) system was used to measure the wake of the MAV wing simultaneously with the signals from the twelve dynamic strain gauges mounted on the wing. With 20 out of 2400 POD modes, a reasonable estimation of the flow flow was observed. By increasing the number of POD modes, a better estimation of the flow field will occur. Utilizing the simultaneously sampled strain gauges and flow field measurements in conjunction with mLSE, an estimation of the flow field with lower energy modes is reasonable. With these results, the methodology for estimating the wake flow field from just dynamic strain gauges is validated.

Particle image and acoustic Doppler velocimetry analysis of a cross-flow turbine wake

NASA Astrophysics Data System (ADS)

Strom, Benjamin; Brunton, Steven; Polagye, Brian

2017-11-01

Cross-flow turbines have advantageous properties for converting kinetic energy in wind and water currents to rotational mechanical energy and subsequently electrical power. A thorough understanding of cross-flow turbine wakes aids understanding of rotor flow physics, assists geometric array design, and informs control strategies for individual turbines in arrays. In this work, the wake physics of a scale model cross-flow turbine are investigated experimentally. Three-component velocity measurements are taken downstream of a two-bladed turbine in a recirculating water channel. Time-resolved stereoscopic particle image and acoustic Doppler velocimetry are compared for planes normal to and distributed along the turbine rotational axis. Wake features are described using proper orthogonal decomposition, dynamic mode decomposition, and the finite-time Lyapunov exponent. Consequences for downstream turbine placement are discussed in conjunction with two-turbine array experiments.

Assessment of swirl spray interaction in lab scale combustor using time-resolved measurements

NASA Astrophysics Data System (ADS)

Rajamanickam, Kuppuraj; Jain, Manish; Basu, Saptarshi

2017-11-01

Liquid fuel injection in highly turbulent swirling flows becomes common practice in gas turbine combustors to improve the flame stabilization. It is well known that the vortex bubble breakdown (VBB) phenomenon in strong swirling jets exhibits complicated flow structures in the spatial domain. In this study, the interaction of hollow cone liquid sheet with such coaxial swirling flow field has been studied experimentally using time-resolved measurements. In particular, much attention is focused towards the near field breakup mechanism (i.e. primary atomization) of liquid sheet. The detailed swirling gas flow field characterization is carried out using time-resolved PIV ( 3.5 kHz). Furthermore, the complicated breakup mechanisms and interaction of the liquid sheet are imaged with the help of high-speed shadow imaging system. Subsequently, proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD) is implemented over the instantaneous data sets to retrieve the modal information associated with the interaction dynamics. This helps to delineate more quantitative nature of interaction process between the liquid sheet and swirling gas phase flow field.

Force Analysis and Energy Operation of Chaotic System of Permanent-Magnet Synchronous Motor

NASA Astrophysics Data System (ADS)

Qi, Guoyuan; Hu, Jianbing

2017-12-01

The disadvantage of a nondimensionalized model of a permanent-magnet synchronous Motor (PMSM) is identified. The original PMSM model is transformed into a Kolmogorov system to aid dynamic force analysis. The vector field of the PMSM is analogous to the force field including four types of torque — inertial, internal, dissipative, and generalized external. Using the feedback thought, the error torque between external torque and dissipative torque is identified. The pitchfork bifurcation of the PMSM is performed. Four forms of energy are identified for the system — kinetic, potential, dissipative, and supplied. The physical interpretations of the decomposition of force and energy exchange are given. Casimir energy is stored energy, and its rate of change is the error power between the dissipative energy and the energy supplied to the motor. Error torque and error power influence the different types of dynamic modes. The Hamiltonian energy and Casimir energy are compared to find the function of each in producing the dynamic modes. A supremum bound for the chaotic attractor is proposed using the error power and Lagrange multiplier.

Image characterization by fractal descriptors in variational mode decomposition domain: Application to brain magnetic resonance

NASA Astrophysics Data System (ADS)

Lahmiri, Salim

2016-08-01

The main purpose of this work is to explore the usefulness of fractal descriptors estimated in multi-resolution domains to characterize biomedical digital image texture. In this regard, three multi-resolution techniques are considered: the well-known discrete wavelet transform (DWT) and the empirical mode decomposition (EMD), and; the newly introduced; variational mode decomposition mode (VMD). The original image is decomposed by the DWT, EMD, and VMD into different scales. Then, Fourier spectrum based fractal descriptors is estimated at specific scales and directions to characterize the image. The support vector machine (SVM) was used to perform supervised classification. The empirical study was applied to the problem of distinguishing between normal and abnormal brain magnetic resonance images (MRI) affected with Alzheimer disease (AD). Our results demonstrate that fractal descriptors estimated in VMD domain outperform those estimated in DWT and EMD domains; and also those directly estimated from the original image.

Adaptive DSPI phase denoising using mutual information and 2D variational mode decomposition

NASA Astrophysics Data System (ADS)

Xiao, Qiyang; Li, Jian; Wu, Sijin; Li, Weixian; Yang, Lianxiang; Dong, Mingli; Zeng, Zhoumo

2018-04-01

In digital speckle pattern interferometry (DSPI), noise interference leads to a low peak signal-to-noise ratio (PSNR) and measurement errors in the phase map. This paper proposes an adaptive DSPI phase denoising method based on two-dimensional variational mode decomposition (2D-VMD) and mutual information. Firstly, the DSPI phase map is subjected to 2D-VMD in order to obtain a series of band-limited intrinsic mode functions (BLIMFs). Then, on the basis of characteristics of the BLIMFs and in combination with mutual information, a self-adaptive denoising method is proposed to obtain noise-free components containing the primary phase information. The noise-free components are reconstructed to obtain the denoising DSPI phase map. Simulation and experimental results show that the proposed method can effectively reduce noise interference, giving a PSNR that is higher than that of two-dimensional empirical mode decomposition methods.

Modal identification of structures by a novel approach based on FDD-wavelet method

NASA Astrophysics Data System (ADS)

Tarinejad, Reza; Damadipour, Majid

2014-02-01

An important application of system identification in structural dynamics is the determination of natural frequencies, mode shapes and damping ratios during operation which can then be used for calibrating numerical models. In this paper, the combination of two advanced methods of Operational Modal Analysis (OMA) called Frequency Domain Decomposition (FDD) and Continuous Wavelet Transform (CWT) based on novel cyclic averaging of correlation functions (CACF) technique are used for identification of dynamic properties. By using this technique, the autocorrelation of averaged correlation functions is used instead of original signals. Integration of FDD and CWT methods is used to overcome their deficiency and take advantage of the unique capabilities of these methods. The FDD method is able to accurately estimate the natural frequencies and mode shapes of structures in the frequency domain. On the other hand, the CWT method is in the time-frequency domain for decomposition of a signal at different frequencies and determines the damping coefficients. In this paper, a new formulation applied to the wavelet transform of the averaged correlation function of an ambient response is proposed. This application causes to accurate estimation of damping ratios from weak (noise) or strong (earthquake) vibrations and long or short duration record. For this purpose, the modified Morlet wavelet having two free parameters is used. The optimum values of these two parameters are obtained by employing a technique which minimizes the entropy of the wavelet coefficients matrix. The capabilities of the novel FDD-Wavelet method in the system identification of various dynamic systems with regular or irregular distribution of mass and stiffness are illustrated. This combined approach is superior to classic methods and yields results that agree well with the exact solutions of the numerical models.

Vibrational spectra of water solutions of azoles from QM/MM calculations: effects of solvation.

PubMed

Tanzi, Luana; Ramondo, Fabio; Guidoni, Leonardo

2012-10-18

Using microsolvation models and mixed quantum/classical ab initio molecular dynamics simulations, we investigate the vibrational properties of two azoles in water solution: pyrazole and oxazole. The effects of the water-azole hydrogen bonding are rationalized by an extensive comparison between structural parameters and harmonic frequencies obtained by microsolvation models. Following the effective normal-mode analysis introduced by Martinez et al. [Martinez et al., J. Chem. Phys. 2006, 125, 144106], we identify the vibrational frequencies of the solutes using the decomposition of the vibrational density of states of the gas phase and solution dynamics. The calculated shifts from gas phase to solution are fairly in agreement with the available experimental data.

Research on Ship-Radiated Noise Denoising Using Secondary Variational Mode Decomposition and Correlation Coefficient.

PubMed

Li, Yuxing; Li, Yaan; Chen, Xiao; Yu, Jing

2017-12-26

As the sound signal of ships obtained by sensors contains other many significant characteristics of ships and called ship-radiated noise (SN), research into a denoising algorithm and its application has obtained great significance. Using the advantage of variational mode decomposition (VMD) combined with the correlation coefficient for denoising, a hybrid secondary denoising algorithm is proposed using secondary VMD combined with a correlation coefficient (CC). First, different kinds of simulation signals are decomposed into several bandwidth-limited intrinsic mode functions (IMFs) using VMD, where the decomposition number by VMD is equal to the number by empirical mode decomposition (EMD); then, the CCs between the IMFs and the simulation signal are calculated respectively. The noise IMFs are identified by the CC threshold and the rest of the IMFs are reconstructed in order to realize the first denoising process. Finally, secondary denoising of the simulation signal can be accomplished by repeating the above steps of decomposition, screening and reconstruction. The final denoising result is determined according to the CC threshold. The denoising effect is compared under the different signal-to-noise ratio and the time of decomposition by VMD. Experimental results show the validity of the proposed denoising algorithm using secondary VMD (2VMD) combined with CC compared to EMD denoising, ensemble EMD (EEMD) denoising, VMD denoising and cubic VMD (3VMD) denoising, as well as two denoising algorithms presented recently. The proposed denoising algorithm is applied to feature extraction and classification for SN signals, which can effectively improve the recognition rate of different kinds of ships.

Thermal Decomposition of Condensed-Phase Nitromethane from Molecular Dynamics from ReaxFF Reactive Dynamics

DTIC Science & Technology

2011-05-04

pubs.acs.org/JPCB Thermal Decomposition of Condensed-Phase Nitromethane from Molecular Dynamics from ReaxFF Reactive Dynamics Si-ping Han,†,‡ Adri C. T. van...ABSTRACT: We studied the thermal decomposition and subsequent reaction of the energetic material nitromethane (CH3NO2) using molec- ular dynamics...with ReaxFF, a first principles-based reactive force field. We characterize the chemistry of liquid and solid nitromethane at high temperatures (2000

Low-Dimensional Model of a Cylinder Wake

NASA Astrophysics Data System (ADS)

Luchtenburg, Mark; Cohen, Kelly; Siegel, Stefan; McLaughlin, Tom

2003-11-01

In a two-dimensional cylinder wake, self-excited oscillations in the form of periodic shedding of vortices are observed above a critical Reynolds number of about 47. These flow-induced non-linear oscillations lead to some undesirable effects associated with unsteady pressures such as fluid-structure interactions. An effective way of suppressing the self-excited flow oscillations is by the incorporation of closed-loop flow control. In this effort, a low dimensional, proper orthogonal decomposition (POD) model is based on data obtained from direct numerical simulations of the Navier Stokes equations for the two dimensional circular cylinder wake at a Reynolds number of 100. Three different conditions are examined, namely, the unforced wake experiencing steady-state vortex shedding, the transient behavior of the unforced wake at the startup of the simulation, and transient response to open-loop harmonic forcing by translation. We discuss POD mode selection and the number of modes that need to be included in the low-dimensional model. It is found that the transient dynamics need to be represented by a coupled system that includes an aperiodic mean-flow mode, an aperiodic shift mode and the periodic von Karman modes. Finally, a least squares mapping method is introduced to develop the non-linear state equations. The predictive capability of the state equations demonstrates the ability of the above approach to model the transient dynamics of the wake.

Design and Parametric Study of the Magnetic Sensor for Position Detection in Linear Motor Based on Nonlinear Parametric Model Order Reduction

PubMed Central

Paul, Sarbajit; Chang, Junghwan

2017-01-01

This paper presents a design approach for a magnetic sensor module to detect mover position using the proper orthogonal decomposition-dynamic mode decomposition (POD-DMD)-based nonlinear parametric model order reduction (PMOR). The parameterization of the sensor module is achieved by using the multipolar moment matching method. Several geometric variables of the sensor module are considered while developing the parametric study. The operation of the sensor module is based on the principle of the airgap flux density distribution detection by the Hall Effect IC. Therefore, the design objective is to achieve a peak flux density (PFD) greater than 0.1 T and total harmonic distortion (THD) less than 3%. To fulfill the constraint conditions, the specifications for the sensor module is achieved by using POD-DMD based reduced model. The POD-DMD based reduced model provides a platform to analyze the high number of design models very fast, with less computational burden. Finally, with the final specifications, the experimental prototype is designed and tested. Two different modes, 90° and 120° modes respectively are used to obtain the position information of the linear motor mover. The position information thus obtained are compared with that of the linear scale data, used as a reference signal. The position information obtained using the 120° mode has a standard deviation of 0.10 mm from the reference linear scale signal, whereas the 90° mode position signal shows a deviation of 0.23 mm from the reference. The deviation in the output arises due to the mechanical tolerances introduced into the specification during the manufacturing process. This provides a scope for coupling the reliability based design optimization in the design process as a future extension. PMID:28671580

Design and Parametric Study of the Magnetic Sensor for Position Detection in Linear Motor Based on Nonlinear Parametric model order reduction.

PubMed

Paul, Sarbajit; Chang, Junghwan

2017-07-01

This paper presents a design approach for a magnetic sensor module to detect mover position using the proper orthogonal decomposition-dynamic mode decomposition (POD-DMD)-based nonlinear parametric model order reduction (PMOR). The parameterization of the sensor module is achieved by using the multipolar moment matching method. Several geometric variables of the sensor module are considered while developing the parametric study. The operation of the sensor module is based on the principle of the airgap flux density distribution detection by the Hall Effect IC. Therefore, the design objective is to achieve a peak flux density (PFD) greater than 0.1 T and total harmonic distortion (THD) less than 3%. To fulfill the constraint conditions, the specifications for the sensor module is achieved by using POD-DMD based reduced model. The POD-DMD based reduced model provides a platform to analyze the high number of design models very fast, with less computational burden. Finally, with the final specifications, the experimental prototype is designed and tested. Two different modes, 90° and 120° modes respectively are used to obtain the position information of the linear motor mover. The position information thus obtained are compared with that of the linear scale data, used as a reference signal. The position information obtained using the 120° mode has a standard deviation of 0.10 mm from the reference linear scale signal, whereas the 90° mode position signal shows a deviation of 0.23 mm from the reference. The deviation in the output arises due to the mechanical tolerances introduced into the specification during the manufacturing process. This provides a scope for coupling the reliability based design optimization in the design process as a future extension.

Energetics and Dynamics of Dissociation of Deprotonated Peptides: Fragmentation of Angiotensin Analogs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Laskin, Julia; Yang, Zhibo

2011-12-01

We present a first study of the energetics and dynamics of dissociation of deprotonated peptides using time- and collision-energy resolved surface-induced dissociation (SID) experiments. SID of four model peptides: RVYIHPF, HVYIHPF, DRVYIHPF, and DHVYIHPF was studied using a specially designed Fourier transform ion cyclotron resonance mass spectrometer (FT-ICR MS) configured for studying ion-surface collisions. Energy and entropy effects for the overall decomposition of the precursor ion were deduced by modeling the time- and collision energy-resolved survival curves using an RRKM based approach developed in our laboratory. The results were compared to the energetics and dynamics of dissociation of the correspondingmore » protonated species. We demonstrate that acidic peptides are less stable in the negative mode because of the low threshold associated with the kinetically hindered loss of H2O from [M-H]- ions. Comparison between the two basic peptides indicates that the lower stability of the [M-H]- ion of RVYIHPF as compared to HVYIHPF towards fragmentation is attributed to the differences in fragmentation mechanisms. Specifically, threshold energy associated with losses of NH3 and NHCNH from RVYIHPF is lower than the barrier for backbone fragmentation that dominates gas-phase decomposition of HVYIHPF. The results provide a first quantitative comparison between the energetics and dynamics of dissociation of [M+H]+ and [M-H]- ions of acidic and basic peptides.« less

Dealing with noise and physiological artifacts in human EEG recordings: empirical mode methods

NASA Astrophysics Data System (ADS)

Runnova, Anastasiya E.; Grubov, Vadim V.; Khramova, Marina V.; Hramov, Alexander E.

2017-04-01

In the paper we propose the new method for removing noise and physiological artifacts in human EEG recordings based on empirical mode decomposition (Hilbert-Huang transform). As physiological artifacts we consider specific oscillatory patterns that cause problems during EEG analysis and can be detected with additional signals recorded simultaneously with EEG (ECG, EMG, EOG, etc.) We introduce the algorithm of the proposed method with steps including empirical mode decomposition of EEG signal, choosing of empirical modes with artifacts, removing these empirical modes and reconstructing of initial EEG signal. We show the efficiency of the method on the example of filtration of human EEG signal from eye-moving artifacts.

Adaptive Fourier decomposition based ECG denoising.

PubMed

Wang, Ze; Wan, Feng; Wong, Chi Man; Zhang, Liming

2016-10-01

A novel ECG denoising method is proposed based on the adaptive Fourier decomposition (AFD). The AFD decomposes a signal according to its energy distribution, thereby making this algorithm suitable for separating pure ECG signal and noise with overlapping frequency ranges but different energy distributions. A stop criterion for the iterative decomposition process in the AFD is calculated on the basis of the estimated signal-to-noise ratio (SNR) of the noisy signal. The proposed AFD-based method is validated by the synthetic ECG signal using an ECG model and also real ECG signals from the MIT-BIH Arrhythmia Database both with additive Gaussian white noise. Simulation results of the proposed method show better performance on the denoising and the QRS detection in comparing with major ECG denoising schemes based on the wavelet transform, the Stockwell transform, the empirical mode decomposition, and the ensemble empirical mode decomposition. Copyright © 2016 Elsevier Ltd. All rights reserved.

Decomposition and nitrogen dynamics of 15N-labeled leaf, root, and twig litter in temperate coniferous forests

Treesearch

T.L. van Huysen; M.E. Harmon; S.S. Perakis; H. Chen

2013-01-01

Litter nutrient dynamics contribute significantly to biogeochemical cycling in forest ecosystems. We examined how site environment and initial substrate quality influence decomposition and nitrogen (N) dynamics of multiple litter types. A 2.5-year decomposition study was installed in the Oregon Coast Range and West Cascades using 15N-labeled...

Application of a Modular Particle-Continuum Method to Partially Rarefied, Hypersonic Flow

NASA Astrophysics Data System (ADS)

Deschenes, Timothy R.; Boyd, Iain D.

2011-05-01

The Modular Particle-Continuum (MPC) method is used to simulate partially-rarefied, hypersonic flow over a sting-mounted planetary probe configuration. This hybrid method uses computational fluid dynamics (CFD) to solve the Navier-Stokes equations in regions that are continuum, while using direct simulation Monte Carlo (DSMC) in portions of the flow that are rarefied. The MPC method uses state-based coupling to pass information between the two flow solvers and decouples both time-step and mesh densities required by each solver. It is parallelized for distributed memory systems using dynamic domain decomposition and internal energy modes can be consistently modeled to be out of equilibrium with the translational mode in both solvers. The MPC results are compared to both full DSMC and CFD predictions and available experimental measurements. By using DSMC in only regions where the flow is nonequilibrium, the MPC method is able to reproduce full DSMC results down to the level of velocity and rotational energy probability density functions while requiring a fraction of the computational time.

Multiscale synchrony behaviors of paired financial time series by 3D multi-continuum percolation

NASA Astrophysics Data System (ADS)

Wang, M.; Wang, J.; Wang, B. T.

2018-02-01

Multiscale synchrony behaviors and nonlinear dynamics of paired financial time series are investigated, in an attempt to study the cross correlation relationships between two stock markets. A random stock price model is developed by a new system called three-dimensional (3D) multi-continuum percolation system, which is utilized to imitate the formation mechanism of price dynamics and explain the nonlinear behaviors found in financial time series. We assume that the price fluctuations are caused by the spread of investment information. The cluster of 3D multi-continuum percolation represents the cluster of investors who share the same investment attitude. In this paper, we focus on the paired return series, the paired volatility series, and the paired intrinsic mode functions which are decomposed by empirical mode decomposition. A new cross recurrence quantification analysis is put forward, combining with multiscale cross-sample entropy, to investigate the multiscale synchrony of these paired series from the proposed model. The corresponding research is also carried out for two China stock markets as comparison.

Explosive decomposition of hydrazine by rapid compression of a gas volume

NASA Technical Reports Server (NTRS)

Bunker, R. L.; Baker, D. L.; Lee, J. H. S.

1991-01-01

In the present investigation of the initiation mechanism and the explosion mode of hydrazine decomposition, a 20 cm-long column of liquid hydrazine was accelerated into a column of gaseous nitrogen, from which it was separated by a thin Teflon diaphragm, in a close-ended cylindrical chamber. Video data obtained reveal the formation of a froth generated by the acceleration of hydrazine into nitrogen at the liquid hydrazine-gaseous nitrogen interface. The explosive hydrazine decomposition had as its initiation mechanism the formation of a froth at a critical temperature; the explosion mode of hydrazine is a confined thermal runaway reaction.

System identification of timber masonry walls using shaking table test

NASA Astrophysics Data System (ADS)

Roy, Timir B.; Guerreiro, Luis; Bagchi, Ashutosh

2017-04-01

Dynamic study is important in order to design, repair and rehabilitation of structures. It has played an important role in the behavior characterization of structures; such as: bridges, dams, high rise buildings etc. There had been substantial development in this area over the last few decades, especially in the field of dynamic identification techniques of structural systems. Frequency Domain Decomposition (FDD) and Time Domain Decomposition are most commonly used methods to identify modal parameters; such as: natural frequency, modal damping and mode shape. The focus of the present research is to study the dynamic characteristics of typical timber masonry walls commonly used in Portugal. For that purpose, a multi-storey structural prototype of such wall has been tested on a seismic shake table at the National Laboratory for Civil Engineering, Portugal (LNEC). Signal processing has been performed of the output response, which is collected from the shaking table experiment of the prototype using accelerometers. In the present work signal processing of the output response, based on the input response has been done in two ways: FDD and Stochastic Subspace Identification (SSI). In order to estimate the values of the modal parameters, algorithms for FDD are formulated and parametric functions for the SSI are computed. Finally, estimated values from both the methods are compared to measure the accuracy of both the techniques.

An Inquiry: Effectiveness of the Complex Empirical Mode Decomposition Method, the Hilbert-Huang Transform, and the Fast-Fourier Transform for Analysis of Dynamic Objects

DTIC Science & Technology

2012-03-01

graphical user interface (GUI) called ALPINE© [18]. Then, it will be converted into a 10 MAT-file that can be read into MATLAB®. At this point...breathing [3]. For comparison purposes, Balocchi et al. recorded the respiratory signal simultaneously with the tachogram (or EKG ) signal. As previously...primary authors, worked to create his own code for implementing the method proposed by Rilling et al. Through reading the BEMD paper and proceeding to

TEMPORAL SIGNATURES OF AIR QUALITY OBSERVATIONS AND MODEL OUTPUTS: DO TIME SERIES DECOMPOSITION METHODS CAPTURE RELEVANT TIME SCALES?

EPA Science Inventory

Time series decomposition methods were applied to meteorological and air quality data and their numerical model estimates. Decomposition techniques express a time series as the sum of a small number of independent modes which hypothetically represent identifiable forcings, thereb...

Forecasting outpatient visits using empirical mode decomposition coupled with back-propagation artificial neural networks optimized by particle swarm optimization

PubMed Central

Huang, Daizheng; Wu, Zhihui

2017-01-01

Accurately predicting the trend of outpatient visits by mathematical modeling can help policy makers manage hospitals effectively, reasonably organize schedules for human resources and finances, and appropriately distribute hospital material resources. In this study, a hybrid method based on empirical mode decomposition and back-propagation artificial neural networks optimized by particle swarm optimization is developed to forecast outpatient visits on the basis of monthly numbers. The data outpatient visits are retrieved from January 2005 to December 2013 and first obtained as the original time series. Second, the original time series is decomposed into a finite and often small number of intrinsic mode functions by the empirical mode decomposition technique. Third, a three-layer back-propagation artificial neural network is constructed to forecast each intrinsic mode functions. To improve network performance and avoid falling into a local minimum, particle swarm optimization is employed to optimize the weights and thresholds of back-propagation artificial neural networks. Finally, the superposition of forecasting results of the intrinsic mode functions is regarded as the ultimate forecasting value. Simulation indicates that the proposed method attains a better performance index than the other four methods. PMID:28222194

Forecasting outpatient visits using empirical mode decomposition coupled with back-propagation artificial neural networks optimized by particle swarm optimization.

PubMed

Huang, Daizheng; Wu, Zhihui

2017-01-01

Accurately predicting the trend of outpatient visits by mathematical modeling can help policy makers manage hospitals effectively, reasonably organize schedules for human resources and finances, and appropriately distribute hospital material resources. In this study, a hybrid method based on empirical mode decomposition and back-propagation artificial neural networks optimized by particle swarm optimization is developed to forecast outpatient visits on the basis of monthly numbers. The data outpatient visits are retrieved from January 2005 to December 2013 and first obtained as the original time series. Second, the original time series is decomposed into a finite and often small number of intrinsic mode functions by the empirical mode decomposition technique. Third, a three-layer back-propagation artificial neural network is constructed to forecast each intrinsic mode functions. To improve network performance and avoid falling into a local minimum, particle swarm optimization is employed to optimize the weights and thresholds of back-propagation artificial neural networks. Finally, the superposition of forecasting results of the intrinsic mode functions is regarded as the ultimate forecasting value. Simulation indicates that the proposed method attains a better performance index than the other four methods.

Multi-scale pixel-based image fusion using multivariate empirical mode decomposition.

PubMed

Rehman, Naveed ur; Ehsan, Shoaib; Abdullah, Syed Muhammad Umer; Akhtar, Muhammad Jehanzaib; Mandic, Danilo P; McDonald-Maier, Klaus D

2015-05-08

A novel scheme to perform the fusion of multiple images using the multivariate empirical mode decomposition (MEMD) algorithm is proposed. Standard multi-scale fusion techniques make a priori assumptions regarding input data, whereas standard univariate empirical mode decomposition (EMD)-based fusion techniques suffer from inherent mode mixing and mode misalignment issues, characterized respectively by either a single intrinsic mode function (IMF) containing multiple scales or the same indexed IMFs corresponding to multiple input images carrying different frequency information. We show that MEMD overcomes these problems by being fully data adaptive and by aligning common frequency scales from multiple channels, thus enabling their comparison at a pixel level and subsequent fusion at multiple data scales. We then demonstrate the potential of the proposed scheme on a large dataset of real-world multi-exposure and multi-focus images and compare the results against those obtained from standard fusion algorithms, including the principal component analysis (PCA), discrete wavelet transform (DWT) and non-subsampled contourlet transform (NCT). A variety of image fusion quality measures are employed for the objective evaluation of the proposed method. We also report the results of a hypothesis testing approach on our large image dataset to identify statistically-significant performance differences.

Multi-Scale Pixel-Based Image Fusion Using Multivariate Empirical Mode Decomposition

PubMed Central

Rehman, Naveed ur; Ehsan, Shoaib; Abdullah, Syed Muhammad Umer; Akhtar, Muhammad Jehanzaib; Mandic, Danilo P.; McDonald-Maier, Klaus D.

2015-01-01

A novel scheme to perform the fusion of multiple images using the multivariate empirical mode decomposition (MEMD) algorithm is proposed. Standard multi-scale fusion techniques make a priori assumptions regarding input data, whereas standard univariate empirical mode decomposition (EMD)-based fusion techniques suffer from inherent mode mixing and mode misalignment issues, characterized respectively by either a single intrinsic mode function (IMF) containing multiple scales or the same indexed IMFs corresponding to multiple input images carrying different frequency information. We show that MEMD overcomes these problems by being fully data adaptive and by aligning common frequency scales from multiple channels, thus enabling their comparison at a pixel level and subsequent fusion at multiple data scales. We then demonstrate the potential of the proposed scheme on a large dataset of real-world multi-exposure and multi-focus images and compare the results against those obtained from standard fusion algorithms, including the principal component analysis (PCA), discrete wavelet transform (DWT) and non-subsampled contourlet transform (NCT). A variety of image fusion quality measures are employed for the objective evaluation of the proposed method. We also report the results of a hypothesis testing approach on our large image dataset to identify statistically-significant performance differences. PMID:26007714

Integrated Network Decompositions and Dynamic Programming for Graph Optimization (INDDGO)

DOE Office of Scientific and Technical Information (OSTI.GOV)

The INDDGO software package offers a set of tools for finding exact solutions to graph optimization problems via tree decompositions and dynamic programming algorithms. Currently the framework offers serial and parallel (distributed memory) algorithms for finding tree decompositions and solving the maximum weighted independent set problem. The parallel dynamic programming algorithm is implemented on top of the MADNESS task-based runtime.

Understanding THz spectra of aqueous solutions: glycine in light and heavy water.

PubMed

Sun, Jian; Niehues, Gudrun; Forbert, Harald; Decka, Dominique; Schwaab, Gerhard; Marx, Dominik; Havenith, Martina

2014-04-02

THz spectroscopy of aqueous solutions has been established as of recently to be a valuable and complementary experimental tool to provide direct insights into the solute-solvent coupling due to hydrogen-bond dynamics involving interfacial water. Despite much experimental progress, understanding THz spectra in terms of molecular motions, akin to mid-infrared spectra, still remains elusive. Here, using the osmoprotectant glycine as a showcase, we demonstrate how this can be achieved by combining THz absorption spectroscopy and ab initio molecular dynamics. The experimental THz spectrum is characterized by broad yet clearly discernible peaks. Based on substantial extensions of available mode-specific decomposition schemes, the experimental spectrum can be reproduced by theory and assigned on an essentially quantitative level. This joint effort reveals an unexpectedly clear picture of the individual contributions of molecular motion to the THz absorption spectrum in terms of distinct modes stemming from intramolecular vibrations, rigid-body-like hindered rotational and translational motion, and specific couplings to interfacial water molecules. The assignment is confirmed by the peak shifts observed in the THz spectrum of deuterated glycine in heavy water, which allow us to separate the distinct modes experimentally.

Identification of Binding Modes for Amino Naphthalene 2-Cyanoacrylate (ANCA) Probes to Amyloid Fibrils from Molecular Dynamics Simulations.

PubMed

He, Huan; Xu, Juan; Cheng, Dan-Yang; Fu, Li; Ge, Yu-Shu; Jiang, Feng-Lei; Liu, Yi

2017-02-16

The amino naphthalene 2-cyanoacrylate (ANCA) probe is a kind of fluorescent amyloid binding probe that can report different fluorescence emissions when bound to various amyloid deposits in tissue, while their interactions with amyloid fibrils remain unclear due to the insoluble nature of amyloid fibrils. Here, all-atom molecular dynamics simulations were used to investigate the interaction between ANCA probes with three different amyloid fibrils. Two common binding modes of ANCA probes on Aβ40 amyloid fibrils were identified by cluster analysis of multiple simulations. The van der Waals and electrostatic interactions were found to be major driving forces for the binding. Atomic contacts analysis and binding free energy decomposition results suggested that the hydrophobic part of ANCA mainly interacts with aromatic side chains on the fibril surface and the hydrophilic part mainly interacts with positive charged residues in the β-sheet region. By comparing the binding modes with different fibrils, we can find that ANCA adopts different conformations while interacting with residues of different hydrophobicity, aromaticity, and electrochemical properties in the β-sheet region, which accounts for its selective mechanism toward different amyloid fibrils.

Rotating Wheel Wake

NASA Astrophysics Data System (ADS)

Lombard, Jean-Eloi; Xu, Hui; Moxey, Dave; Sherwin, Spencer

2016-11-01

For open wheel race-cars, such as Formula One, or IndyCar, the wheels are responsible for 40 % of the total drag. For road cars, drag associated to the wheels and under-carriage can represent 20 - 60 % of total drag at highway cruise speeds. Experimental observations have reported two, three or more pairs of counter rotating vortices, the relative strength of which still remains an open question. The near wake of an unsteady rotating wheel. The numerical investigation by means of direct numerical simulation at ReD =400-1000 is presented here to further the understanding of bifurcations the flow undergoes as the Reynolds number is increased. Direct numerical simulation is performed using Nektar++, the results of which are compared to those of Pirozzoli et al. (2012). Both proper orthogonal decomposition and dynamic mode decomposition, as well as spectral analysis are leveraged to gain unprecedented insight into the bifurcations and subsequent topological differences of the wake as the Reynolds number is increased.

Model and Data Reduction for Control, Identification and Compressed Sensing

NASA Astrophysics Data System (ADS)

Kramer, Boris

This dissertation focuses on problems in design, optimization and control of complex, large-scale dynamical systems from different viewpoints. The goal is to develop new algorithms and methods, that solve real problems more efficiently, together with providing mathematical insight into the success of those methods. There are three main contributions in this dissertation. In Chapter 3, we provide a new method to solve large-scale algebraic Riccati equations, which arise in optimal control, filtering and model reduction. We present a projection based algorithm utilizing proper orthogonal decomposition, which is demonstrated to produce highly accurate solutions at low rank. The method is parallelizable, easy to implement for practitioners, and is a first step towards a matrix free approach to solve AREs. Numerical examples for n ≥ 106 unknowns are presented. In Chapter 4, we develop a system identification method which is motivated by tangential interpolation. This addresses the challenge of fitting linear time invariant systems to input-output responses of complex dynamics, where the number of inputs and outputs is relatively large. The method reduces the computational burden imposed by a full singular value decomposition, by carefully choosing directions on which to project the impulse response prior to assembly of the Hankel matrix. The identification and model reduction step follows from the eigensystem realization algorithm. We present three numerical examples, a mass spring damper system, a heat transfer problem, and a fluid dynamics system. We obtain error bounds and stability results for this method. Chapter 5 deals with control and observation design for parameter dependent dynamical systems. We address this by using local parametric reduced order models, which can be used online. Data available from simulations of the system at various configurations (parameters, boundary conditions) is used to extract a sparse basis to represent the dynamics (via dynamic mode decomposition). Subsequently, a new, compressed sensing based classification algorithm is developed which incorporates the extracted dynamic information into the sensing basis. We show that this augmented classification basis makes the method more robust to noise, and results in superior identification of the correct parameter. Numerical examples consist of a Navier-Stokes, as well as a Boussinesq flow application.

A Four-Stage Hybrid Model for Hydrological Time Series Forecasting

PubMed Central

Di, Chongli; Yang, Xiaohua; Wang, Xiaochao

2014-01-01

Hydrological time series forecasting remains a difficult task due to its complicated nonlinear, non-stationary and multi-scale characteristics. To solve this difficulty and improve the prediction accuracy, a novel four-stage hybrid model is proposed for hydrological time series forecasting based on the principle of ‘denoising, decomposition and ensemble’. The proposed model has four stages, i.e., denoising, decomposition, components prediction and ensemble. In the denoising stage, the empirical mode decomposition (EMD) method is utilized to reduce the noises in the hydrological time series. Then, an improved method of EMD, the ensemble empirical mode decomposition (EEMD), is applied to decompose the denoised series into a number of intrinsic mode function (IMF) components and one residual component. Next, the radial basis function neural network (RBFNN) is adopted to predict the trend of all of the components obtained in the decomposition stage. In the final ensemble prediction stage, the forecasting results of all of the IMF and residual components obtained in the third stage are combined to generate the final prediction results, using a linear neural network (LNN) model. For illustration and verification, six hydrological cases with different characteristics are used to test the effectiveness of the proposed model. The proposed hybrid model performs better than conventional single models, the hybrid models without denoising or decomposition and the hybrid models based on other methods, such as the wavelet analysis (WA)-based hybrid models. In addition, the denoising and decomposition strategies decrease the complexity of the series and reduce the difficulties of the forecasting. With its effective denoising and accurate decomposition ability, high prediction precision and wide applicability, the new model is very promising for complex time series forecasting. This new forecast model is an extension of nonlinear prediction models. PMID:25111782

A four-stage hybrid model for hydrological time series forecasting.

PubMed

Di, Chongli; Yang, Xiaohua; Wang, Xiaochao

2014-01-01

Hydrological time series forecasting remains a difficult task due to its complicated nonlinear, non-stationary and multi-scale characteristics. To solve this difficulty and improve the prediction accuracy, a novel four-stage hybrid model is proposed for hydrological time series forecasting based on the principle of 'denoising, decomposition and ensemble'. The proposed model has four stages, i.e., denoising, decomposition, components prediction and ensemble. In the denoising stage, the empirical mode decomposition (EMD) method is utilized to reduce the noises in the hydrological time series. Then, an improved method of EMD, the ensemble empirical mode decomposition (EEMD), is applied to decompose the denoised series into a number of intrinsic mode function (IMF) components and one residual component. Next, the radial basis function neural network (RBFNN) is adopted to predict the trend of all of the components obtained in the decomposition stage. In the final ensemble prediction stage, the forecasting results of all of the IMF and residual components obtained in the third stage are combined to generate the final prediction results, using a linear neural network (LNN) model. For illustration and verification, six hydrological cases with different characteristics are used to test the effectiveness of the proposed model. The proposed hybrid model performs better than conventional single models, the hybrid models without denoising or decomposition and the hybrid models based on other methods, such as the wavelet analysis (WA)-based hybrid models. In addition, the denoising and decomposition strategies decrease the complexity of the series and reduce the difficulties of the forecasting. With its effective denoising and accurate decomposition ability, high prediction precision and wide applicability, the new model is very promising for complex time series forecasting. This new forecast model is an extension of nonlinear prediction models.

Synchronous Motions Across the Instrumental Climate Record

NASA Astrophysics Data System (ADS)

Carl, Peter

The Earth's climate system bears a rich variety of feedback mechanisms that may give rise to complex, evolving modal structures under internal and external control. Various types of synchronization may be identified in the system's motion when looking at representative time series of the instrumental period through the glasses of an advanced technique of sparse data approximation, the Matching Pursuit (MP) approach. To disentangle the emerging network of oscillatory modes to the degree that climate dynamics turns out to be separable, a large dictionary of "Gaussian logons," i.e. frequency modulated (FM) Gabor atoms, is applied. Though the extracted modes make up linear decompositions, this flexible analyzing signal matches highly nonlinear waveforms. Univariate analyses over the period 1870-1997 are presented of a set of customary time series in annual resolution, comprising global and regional climate, central European synoptic systems, German precipitation, and runoff of the Elbe river near Dresden. All the evidence from this first-generation MP-FM study, obtained in subsequent multivariate syntheses, points to dynamically excited regimes of an organized yet complex climate system under permanent change—perhaps a (pre)chaotic one at centennial timescales, suggesting a "chaos control" perspective on global climate dynamics and change. Findings and conclusions include, among others, internal structure of reconstructed insolation, the episodic nature of global warming as reflected in multidecadal temperature modes, their swarm of "interdomain" companions across the whole system that unveils an unknown regime character of interannual climate dynamics, and the apparent onset early in the 1990s of the present thermal stagnation.

VELOCITY FIELD OF COMPRESSIBLE MAGNETOHYDRODYNAMIC TURBULENCE: WAVELET DECOMPOSITION AND MODE SCALINGS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kowal, Grzegorz; Lazarian, A., E-mail: kowal@astro.wisc.ed, E-mail: lazarian@astro.wisc.ed

We study compressible magnetohydrodynamic turbulence, which holds the key to many astrophysical processes, including star formation and cosmic-ray propagation. To account for the variations of the magnetic field in the strongly turbulent fluid, we use wavelet decomposition of the turbulent velocity field into Alfven, slow, and fast modes, which presents an extension of the Cho and Lazarian decomposition approach based on Fourier transforms. The wavelets allow us to follow the variations of the local direction of the magnetic field and therefore improve the quality of the decomposition compared to the Fourier transforms, which are done in the mean field referencemore » frame. For each resulting component, we calculate the spectra and two-point statistics such as longitudinal and transverse structure functions as well as higher order intermittency statistics. In addition, we perform a Helmholtz- Hodge decomposition of the velocity field into incompressible and compressible parts and analyze these components. We find that the turbulence intermittency is different for different components, and we show that the intermittency statistics depend on whether the phenomenon was studied in the global reference frame related to the mean magnetic field or in the frame defined by the local magnetic field. The dependencies of the measures we obtained are different for different components of the velocity; for instance, we show that while the Alfven mode intermittency changes marginally with the Mach number, the intermittency of the fast mode is substantially affected by the change.« less

Rotational dynamics of polyatomic ions in aqueous solutions: From continuum model to mode-coupling theory, aided by computer simulations.

PubMed

Banerjee, Puja; Bagchi, Biman

2018-06-14

Due to the presence of the rotational mode and the distributed surface charges, the dynamical behavior of polyatomic ions in water differs considerably from those of the monatomic ions. However, their fascinating dynamical properties have drawn scant attention. We carry out theoretical and computational studies of a series of well-known polyatomic ions, namely, sulfate, nitrate, and acetate ions. All three ions exhibit different rotational diffusivity, with that of the nitrate ion being considerably larger than the other two. They all defy the hydrodynamic laws of size dependence. Study of the local structure around the ions provides valuable insight into the origin of these differences. We carry out a detailed study of the rotational diffusion of these ions by extensive computer simulation and by using the theoretical approaches of the dielectric friction developed by Fatuzzo-Mason (FM) and Nee-Zwanzig (NZ), and subsequently generalized by Alavi and Waldeck. A critical element of the FM-NZ theory is the decomposition of the total rotational friction, ζ Rot , into Stokes and dielectric parts. The study shows a dominant role of dielectric friction in the sense that if the ions are made neutral, the nature of diffusion changes and the values become much larger. Our analyses further reveal that the decomposition of total friction into the Stokes and dielectric friction breaks down for sulfate ions but remains semi-quantitatively valid for nitrate and acetate ions. We discuss the relationship between translational and rotational dielectric friction on rigid spherical ions. We develop a self-consistent mode-coupling theory (SC-MCT) formalism that could provide a unified view of rotational friction of polyatomic ions in polar medium. Our SC-MCT shows that the breakdown can be attributed to the change in the microscopic structural features. The mode-coupling theory helps in elucidating the role of coupling between translational and rotational motion of these ions. In fact, these two motions self-consistently determine the value of each other. The reference interaction site model-based MCT suggests an interesting relation between the torque-torque and the force-force time correlation function with the proportionality constant being determined by the geometry and the charge distribution of the polyatomic molecule. We point out several parallelisms between the theories of translational and rotation friction calculations of ions in polar liquids.

Palm vein recognition based on directional empirical mode decomposition

NASA Astrophysics Data System (ADS)

Lee, Jen-Chun; Chang, Chien-Ping; Chen, Wei-Kuei

2014-04-01

Directional empirical mode decomposition (DEMD) has recently been proposed to make empirical mode decomposition suitable for the processing of texture analysis. Using DEMD, samples are decomposed into a series of images, referred to as two-dimensional intrinsic mode functions (2-D IMFs), from finer to large scale. A DEMD-based 2 linear discriminant analysis (LDA) for palm vein recognition is proposed. The proposed method progresses through three steps: (i) a set of 2-D IMF features of various scale and orientation are extracted using DEMD, (ii) the 2LDA method is then applied to reduce the dimensionality of the feature space in both the row and column directions, and (iii) the nearest neighbor classifier is used for classification. We also propose two strategies for using the set of 2-D IMF features: ensemble DEMD vein representation (EDVR) and multichannel DEMD vein representation (MDVR). In experiments using palm vein databases, the proposed MDVR-based 2LDA method achieved recognition accuracy of 99.73%, thereby demonstrating its feasibility for palm vein recognition.

Nutrient dynamics and decomposition of riparian Arundinaria gigantea (Walt.)Muhl. leaves in southern Illinois

USDA-ARS?s Scientific Manuscript database

Leaf litter quality and quantity can influence soil nutrient dynamics and stream productivity through decomposition and serving as allochthonous stream inputs. Leaf deposition, nitrogen (N)-resorption efficiency and proficiency, and decomposition rates were analyzed in riparian stands of Arundinaria...

A novel hybrid decomposition-and-ensemble model based on CEEMD and GWO for short-term PM2.5 concentration forecasting

NASA Astrophysics Data System (ADS)

Niu, Mingfei; Wang, Yufang; Sun, Shaolong; Li, Yongwu

2016-06-01

To enhance prediction reliability and accuracy, a hybrid model based on the promising principle of "decomposition and ensemble" and a recently proposed meta-heuristic called grey wolf optimizer (GWO) is introduced for daily PM2.5 concentration forecasting. Compared with existing PM2.5 forecasting methods, this proposed model has improved the prediction accuracy and hit rates of directional prediction. The proposed model involves three main steps, i.e., decomposing the original PM2.5 series into several intrinsic mode functions (IMFs) via complementary ensemble empirical mode decomposition (CEEMD) for simplifying the complex data; individually predicting each IMF with support vector regression (SVR) optimized by GWO; integrating all predicted IMFs for the ensemble result as the final prediction by another SVR optimized by GWO. Seven benchmark models, including single artificial intelligence (AI) models, other decomposition-ensemble models with different decomposition methods and models with the same decomposition-ensemble method but optimized by different algorithms, are considered to verify the superiority of the proposed hybrid model. The empirical study indicates that the proposed hybrid decomposition-ensemble model is remarkably superior to all considered benchmark models for its higher prediction accuracy and hit rates of directional prediction.

Dynamic Data-Driven Reduced-Order Models of Macroscale Quantities for the Prediction of Equilibrium System State for Multiphase Porous Medium Systems

NASA Astrophysics Data System (ADS)

Talbot, C.; McClure, J. E.; Armstrong, R. T.; Mostaghimi, P.; Hu, Y.; Miller, C. T.

2017-12-01

Microscale simulation of multiphase flow in realistic, highly-resolved porous medium systems of a sufficient size to support macroscale evaluation is computationally demanding. Such approaches can, however, reveal the dynamic, steady, and equilibrium states of a system. We evaluate methods to utilize dynamic data to reduce the cost associated with modeling a steady or equilibrium state. We construct data-driven models using extensions to dynamic mode decomposition (DMD) and its connections to Koopman Operator Theory. DMD and its variants comprise a class of equation-free methods for dimensionality reduction of time-dependent nonlinear dynamical systems. DMD furnishes an explicit reduced representation of system states in terms of spatiotemporally varying modes with time-dependent oscillation frequencies and amplitudes. We use DMD to predict the steady and equilibrium macroscale state of a realistic two-fluid porous medium system imaged using micro-computed tomography (µCT) and simulated using the lattice Boltzmann method (LBM). We apply Koopman DMD to direct numerical simulation data resulting from simulations of multiphase fluid flow through a 1440x1440x4320 section of a full 1600x1600x5280 realization of imaged sandstone. We determine a representative set of system observables via dimensionality reduction techniques including linear and kernel principal component analysis. We demonstrate how this subset of macroscale quantities furnishes a representation of the time-evolution of the system in terms of dynamic modes, and discuss the selection of a subset of DMD modes yielding the optimal reduced model, as well as the time-dependence of the error in the predicted equilibrium value of each macroscale quantity. Finally, we describe how the above procedure, modified to incorporate methods from compressed sensing and random projection techniques, may be used in an online fashion to facilitate adaptive time-stepping and parsimonious storage of system states over time.

Two Dimensional Finite Element Based Magnetotelluric Inversion using Singular Value Decomposition Method on Transverse Electric Mode

NASA Astrophysics Data System (ADS)

Tjong, Tiffany; Yihaa’ Roodhiyah, Lisa; Nurhasan; Sutarno, Doddy

2018-04-01

In this work, an inversion scheme was performed using a vector finite element (VFE) based 2-D magnetotelluric (MT) forward modelling. We use an inversion scheme with Singular value decomposition (SVD) method toimprove the accuracy of MT inversion.The inversion scheme was applied to transverse electric (TE) mode of MT. SVD method was used in this inversion to decompose the Jacobian matrices. Singular values which obtained from the decomposition process were analyzed. This enabled us to determine the importance of data and therefore to define a threshold for truncation process. The truncation of singular value in inversion processcould improve the resulted model.

Predicting Flow Reversals in a Computational Fluid Dynamics Simulated Thermosyphon Using Data Assimilation.

PubMed

Reagan, Andrew J; Dubief, Yves; Dodds, Peter Sheridan; Danforth, Christopher M

2016-01-01

A thermal convection loop is a annular chamber filled with water, heated on the bottom half and cooled on the top half. With sufficiently large forcing of heat, the direction of fluid flow in the loop oscillates chaotically, dynamics analogous to the Earth's weather. As is the case for state-of-the-art weather models, we only observe the statistics over a small region of state space, making prediction difficult. To overcome this challenge, data assimilation (DA) methods, and specifically ensemble methods, use the computational model itself to estimate the uncertainty of the model to optimally combine these observations into an initial condition for predicting the future state. Here, we build and verify four distinct DA methods, and then, we perform a twin model experiment with the computational fluid dynamics simulation of the loop using the Ensemble Transform Kalman Filter (ETKF) to assimilate observations and predict flow reversals. We show that using adaptively shaped localized covariance outperforms static localized covariance with the ETKF, and allows for the use of less observations in predicting flow reversals. We also show that a Dynamic Mode Decomposition (DMD) of the temperature and velocity fields recovers the low dimensional system underlying reversals, finding specific modes which together are predictive of reversal direction.

Predicting Flow Reversals in a Computational Fluid Dynamics Simulated Thermosyphon Using Data Assimilation

PubMed Central

Reagan, Andrew J.; Dubief, Yves; Dodds, Peter Sheridan; Danforth, Christopher M.

2016-01-01

A thermal convection loop is a annular chamber filled with water, heated on the bottom half and cooled on the top half. With sufficiently large forcing of heat, the direction of fluid flow in the loop oscillates chaotically, dynamics analogous to the Earth’s weather. As is the case for state-of-the-art weather models, we only observe the statistics over a small region of state space, making prediction difficult. To overcome this challenge, data assimilation (DA) methods, and specifically ensemble methods, use the computational model itself to estimate the uncertainty of the model to optimally combine these observations into an initial condition for predicting the future state. Here, we build and verify four distinct DA methods, and then, we perform a twin model experiment with the computational fluid dynamics simulation of the loop using the Ensemble Transform Kalman Filter (ETKF) to assimilate observations and predict flow reversals. We show that using adaptively shaped localized covariance outperforms static localized covariance with the ETKF, and allows for the use of less observations in predicting flow reversals. We also show that a Dynamic Mode Decomposition (DMD) of the temperature and velocity fields recovers the low dimensional system underlying reversals, finding specific modes which together are predictive of reversal direction. PMID:26849061

Algorithm for Stabilizing a POD-Based Dynamical System

NASA Technical Reports Server (NTRS)

Kalb, Virginia L.

2010-01-01

This algorithm provides a new way to improve the accuracy and asymptotic behavior of a low-dimensional system based on the proper orthogonal decomposition (POD). Given a data set representing the evolution of a system of partial differential equations (PDEs), such as the Navier-Stokes equations for incompressible flow, one may obtain a low-dimensional model in the form of ordinary differential equations (ODEs) that should model the dynamics of the flow. Temporal sampling of the direct numerical simulation of the PDEs produces a spatial time series. The POD extracts the temporal and spatial eigenfunctions of this data set. Truncated to retain only the most energetic modes followed by Galerkin projection of these modes onto the PDEs obtains a dynamical system of ordinary differential equations for the time-dependent behavior of the flow. In practice, the steps leading to this system of ODEs entail numerically computing first-order derivatives of the mean data field and the eigenfunctions, and the computation of many inner products. This is far from a perfect process, and often results in the lack of long-term stability of the system and incorrect asymptotic behavior of the model. This algorithm describes a new stabilization method that utilizes the temporal eigenfunctions to derive correction terms for the coefficients of the dynamical system to significantly reduce these errors.

Denoising of chaotic signal using independent component analysis and empirical mode decomposition with circulate translating

NASA Astrophysics Data System (ADS)

Wen-Bo, Wang; Xiao-Dong, Zhang; Yuchan, Chang; Xiang-Li, Wang; Zhao, Wang; Xi, Chen; Lei, Zheng

2016-01-01

In this paper, a new method to reduce noises within chaotic signals based on ICA (independent component analysis) and EMD (empirical mode decomposition) is proposed. The basic idea is decomposing chaotic signals and constructing multidimensional input vectors, firstly, on the base of EMD and its translation invariance. Secondly, it makes the independent component analysis on the input vectors, which means that a self adapting denoising is carried out for the intrinsic mode functions (IMFs) of chaotic signals. Finally, all IMFs compose the new denoised chaotic signal. Experiments on the Lorenz chaotic signal composed of different Gaussian noises and the monthly observed chaotic sequence on sunspots were put into practice. The results proved that the method proposed in this paper is effective in denoising of chaotic signals. Moreover, it can correct the center point in the phase space effectively, which makes it approach the real track of the chaotic attractor. Project supported by the National Science and Technology, China (Grant No. 2012BAJ15B04), the National Natural Science Foundation of China (Grant Nos. 41071270 and 61473213), the Natural Science Foundation of Hubei Province, China (Grant No. 2015CFB424), the State Key Laboratory Foundation of Satellite Ocean Environment Dynamics, China (Grant No. SOED1405), the Hubei Provincial Key Laboratory Foundation of Metallurgical Industry Process System Science, China (Grant No. Z201303), and the Hubei Key Laboratory Foundation of Transportation Internet of Things, Wuhan University of Technology, China (Grant No.2015III015-B02).

Modal identification of spindle-tool unit in high-speed machining

NASA Astrophysics Data System (ADS)

Gagnol, Vincent; Le, Thien-Phu; Ray, Pascal

2011-10-01

The accurate knowledge of high-speed motorised spindle dynamic behaviour during machining is important in order to ensure the reliability of machine tools in service and the quality of machined parts. More specifically, the prediction of stable cutting regions, which is a critical requirement for high-speed milling operations, requires the accurate estimation of tool/holder/spindle set dynamic modal parameters. These estimations are generally obtained through Frequency Response Function (FRF) measurements of the non-rotating spindle. However, significant changes in modal parameters are expected to occur during operation, due to high-speed spindle rotation. The spindle's modal variations are highlighted through an integrated finite element model of the dynamic high-speed spindle-bearing system, taking into account rotor dynamics effects. The dependency of dynamic behaviour on speed range is then investigated and determined with accuracy. The objective of the proposed paper is to validate these numerical results through an experiment-based approach. Hence, an experimental setup is elaborated to measure rotating tool vibration during the machining operation in order to determine the spindle's modal frequency variation with respect to spindle speed in an industrial environment. The identification of natural frequencies of the spindle under rotating conditions is challenging, due to the low number of sensors and the presence of many harmonics in the measured signals. In order to overcome these issues and to extract the characteristics of the system, the spindle modes are determined through a 3-step procedure. First, spindle modes are highlighted using the Frequency Domain Decomposition (FDD) technique, with a new formulation at the considered rotating speed. These extracted modes are then analysed through the value of their respective damping ratios in order to separate the harmonics component from structural spindle natural frequencies. Finally, the stochastic properties of the modes are also investigated by considering the probability density of the retained modes. Results show a good correlation between numerical and experiment-based identified frequencies. The identified spindle-tool modal properties during machining allow the numerical model to be considered as representative of the real dynamic properties of the system.

Koopman operator theory: Past, present, and future

NASA Astrophysics Data System (ADS)

Brunton, Steven; Kaiser, Eurika; Kutz, Nathan

2017-11-01

Koopman operator theory has emerged as a dominant method to represent nonlinear dynamics in terms of an infinite-dimensional linear operator. The Koopman operator acts on the space of all possible measurement functions of the system state, advancing these measurements with the flow of the dynamics. A linear representation of nonlinear dynamics has tremendous potential to enable the prediction, estimation, and control of nonlinear systems with standard textbook methods developed for linear systems. Dynamic mode decomposition has become the leading data-driven method to approximate the Koopman operator, although there are still open questions and challenges around how to obtain accurate approximations for strongly nonlinear systems. This talk will provide an introductory overview of modern Koopman operator theory, reviewing the basics and describing recent theoretical and algorithmic developments. Particular emphasis will be placed on the use of data-driven Koopman theory to characterize and control high-dimensional fluid dynamic systems. This talk will also address key advances in the rapidly growing fields of machine learning and data science that are likely to drive future developments.

An Alternate Method for Estimating Dynamic Height from XBT Profiles Using Empirical Vertical Modes

NASA Technical Reports Server (NTRS)

Lagerloef, Gary S. E.

1994-01-01

A technique is presented that applies modal decomposition to estimate dynamic height (0-450 db) from Expendable BathyThermograph (XBT) temperature profiles. Salinity-Temperature-Depth (STD) data are used to establish empirical relationships between vertically integrated temperature profiles and empirical dynamic height modes. These are then applied to XBT data to estimate dynamic height. A standard error of 0.028 dynamic meters is obtained for the waters of the Gulf of Alaska- an ocean region subject to substantial freshwater buoyancy forcing and with a T-S relationship that has considerable scatter. The residual error is a substantial improvement relative to the conventional T-S correlation technique when applied to this region. Systematic errors between estimated and true dynamic height were evaluated. The 20-year-long time series at Ocean Station P (50 deg N, 145 deg W) indicated weak variations in the error interannually, but not seasonally. There were no evident systematic alongshore variations in the error in the ocean boundary current regime near the perimeter of the Alaska gyre. The results prove satisfactory for the purpose of this work, which is to generate dynamic height from XBT data for coanalysis with satellite altimeter data, given that the altimeter height precision is likewise on the order of 2-3 cm. While the technique has not been applied to other ocean regions where the T-S relation has less scatter, it is suggested that it could provide some improvement over previously applied methods, as well.

Filtration of human EEG recordings from physiological artifacts with empirical mode method

NASA Astrophysics Data System (ADS)

Grubov, Vadim V.; Runnova, Anastasiya E.; Khramova, Marina V.

2017-03-01

In the paper we propose the new method for dealing with noise and physiological artifacts in experimental human EEG recordings. The method is based on analysis of EEG signals with empirical mode decomposition (Hilbert-Huang transform). We consider noises and physiological artifacts on EEG as specific oscillatory patterns that cause problems during EEG analysis and can be detected with additional signals recorded simultaneously with EEG (ECG, EMG, EOG, etc.) We introduce the algorithm of the method with following steps: empirical mode decomposition of EEG signal, choosing of empirical modes with artifacts, removing empirical modes with artifacts, reconstruction of the initial EEG signal. We test the method on filtration of experimental human EEG signals from eye-moving artifacts and show high efficiency of the method.

Computer implemented empirical mode decomposition method, apparatus, and article of manufacture for two-dimensional signals

NASA Technical Reports Server (NTRS)

Huang, Norden E. (Inventor)

2001-01-01

A computer implemented method of processing two-dimensional physical signals includes five basic components and the associated presentation techniques of the results. The first component decomposes the two-dimensional signal into one-dimensional profiles. The second component is a computer implemented Empirical Mode Decomposition that extracts a collection of Intrinsic Mode Functions (IMF's) from each profile based on local extrema and/or curvature extrema. The decomposition is based on the direct extraction of the energy associated with various intrinsic time scales in the profiles. In the third component, the IMF's of each profile are then subjected to a Hilbert Transform. The fourth component collates the Hilbert transformed IMF's of the profiles to form a two-dimensional Hilbert Spectrum. A fifth component manipulates the IMF's by, for example, filtering the two-dimensional signal by reconstructing the two-dimensional signal from selected IMF(s).

Tissue artifact removal from respiratory signals based on empirical mode decomposition.

PubMed

Liu, Shaopeng; Gao, Robert X; John, Dinesh; Staudenmayer, John; Freedson, Patty

2013-05-01

On-line measurement of respiration plays an important role in monitoring human physical activities. Such measurement commonly employs sensing belts secured around the rib cage and abdomen of the test object. Affected by the movement of body tissues, respiratory signals typically have a low signal-to-noise ratio. Removing tissue artifacts therefore is critical to ensuring effective respiration analysis. This paper presents a signal decomposition technique for tissue artifact removal from respiratory signals, based on the empirical mode decomposition (EMD). An algorithm based on the mutual information and power criteria was devised to automatically select appropriate intrinsic mode functions for tissue artifact removal and respiratory signal reconstruction. Performance of the EMD-algorithm was evaluated through simulations and real-life experiments (N = 105). Comparison with low-pass filtering that has been conventionally applied confirmed the effectiveness of the technique in tissue artifacts removal.

A novel hybrid model for air quality index forecasting based on two-phase decomposition technique and modified extreme learning machine.

PubMed

Wang, Deyun; Wei, Shuai; Luo, Hongyuan; Yue, Chenqiang; Grunder, Olivier

2017-02-15

The randomness, non-stationarity and irregularity of air quality index (AQI) series bring the difficulty of AQI forecasting. To enhance forecast accuracy, a novel hybrid forecasting model combining two-phase decomposition technique and extreme learning machine (ELM) optimized by differential evolution (DE) algorithm is developed for AQI forecasting in this paper. In phase I, the complementary ensemble empirical mode decomposition (CEEMD) is utilized to decompose the AQI series into a set of intrinsic mode functions (IMFs) with different frequencies; in phase II, in order to further handle the high frequency IMFs which will increase the forecast difficulty, variational mode decomposition (VMD) is employed to decompose the high frequency IMFs into a number of variational modes (VMs). Then, the ELM model optimized by DE algorithm is applied to forecast all the IMFs and VMs. Finally, the forecast value of each high frequency IMF is obtained through adding up the forecast results of all corresponding VMs, and the forecast series of AQI is obtained by aggregating the forecast results of all IMFs. To verify and validate the proposed model, two daily AQI series from July 1, 2014 to June 30, 2016 collected from Beijing and Shanghai located in China are taken as the test cases to conduct the empirical study. The experimental results show that the proposed hybrid model based on two-phase decomposition technique is remarkably superior to all other considered models for its higher forecast accuracy. Copyright © 2016 Elsevier B.V. All rights reserved.

Molecular dynamics simulations of methane hydrate decomposition.

PubMed

Myshakin, Evgeniy M; Jiang, Hao; Warzinski, Robert P; Jordan, Kenneth D

2009-03-12

Molecular dynamics simulations have been carried out to study decomposition of methane hydrate at different cage occupancies. The decomposition rate is found to depend sensitively on the hydration number. The rate of the destruction of the cages displays Arrhenius behavior, consistent with an activated mechanism. During the simulations, reversible formation of partial water cages around methane molecules in the liquid was observed at the interface at temperatures above the computed hydrate decomposition temperature.

Phase space interrogation of the empirical response modes for seismically excited structures

NASA Astrophysics Data System (ADS)

Paul, Bibhas; George, Riya C.; Mishra, Sudib K.

2017-07-01

Conventional Phase Space Interrogation (PSI) for structural damage assessment relies on exciting the structure with low dimensional chaotic waveform, thereby, significantly limiting their applicability to large structures. The PSI technique is presently extended for structure subjected to seismic excitations. The high dimensionality of the phase space for seismic response(s) are overcome by the Empirical Mode Decomposition (EMD), decomposing the responses to a number of intrinsic low dimensional oscillatory modes, referred as Intrinsic Mode Functions (IMFs). Along with their low dimensionality, a few IMFs, retain sufficient information of the system dynamics to reflect the damage induced changes. The mutually conflicting nature of low-dimensionality and the sufficiency of dynamic information are taken care by the optimal choice of the IMF(s), which is shown to be the third/fourth IMFs. The optimal IMF(s) are employed for the reconstruction of the Phase space attractor following Taken's embedding theorem. The widely referred Changes in Phase Space Topology (CPST) feature is then employed on these Phase portrait(s) to derive the damage sensitive feature, referred as the CPST of the IMFs (CPST-IMF). The legitimacy of the CPST-IMF is established as a damage sensitive feature by assessing its variation with a number of damage scenarios benchmarked in the IASC-ASCE building. The damage localization capability, remarkable tolerance to noise contamination and the robustness under different seismic excitations of the feature are demonstrated.

Decomposition-Based Failure Mode Identification Method for Risk-Free Design of Large Systems

NASA Technical Reports Server (NTRS)

Tumer, Irem Y.; Stone, Robert B.; Roberts, Rory A.; Clancy, Daniel (Technical Monitor)

2002-01-01

When designing products, it is crucial to assure failure and risk-free operation in the intended operating environment. Failures are typically studied and eliminated as much as possible during the early stages of design. The few failures that go undetected result in unacceptable damage and losses in high-risk applications where public safety is of concern. Published NASA and NTSB accident reports point to a variety of components identified as sources of failures in the reported cases. In previous work, data from these reports were processed and placed in matrix form for all the system components and failure modes encountered, and then manipulated using matrix methods to determine similarities between the different components and failure modes. In this paper, these matrices are represented in the form of a linear combination of failures modes, mathematically formed using Principal Components Analysis (PCA) decomposition. The PCA decomposition results in a low-dimensionality representation of all failure modes and components of interest, represented in a transformed coordinate system. Such a representation opens the way for efficient pattern analysis and prediction of failure modes with highest potential risks on the final product, rather than making decisions based on the large space of component and failure mode data. The mathematics of the proposed method are explained first using a simple example problem. The method is then applied to component failure data gathered from helicopter, accident reports to demonstrate its potential.

Investigation of coherent structures in a superheated jet using decomposition methods

NASA Astrophysics Data System (ADS)

Sinha, Avick; Gopalakrishnan, Shivasubramanian; Balasubramanian, Sridhar

2016-11-01

A superheated turbulent jet, commonly encountered in many engineering flows, is complex two phase mixture of liquid and vapor. The superposition of temporally and spatially evolving coherent vortical motions, known as coherent structures (CS), govern the dynamics of such a jet. Both POD and DMD are employed to analyze such vortical motions. PIV data is used in conjunction with the decomposition methods to analyze the CS in the flow. The experiments were conducted using water emanating into a tank containing homogeneous fluid at ambient condition. Three inlet pressure were employed in the study, all at a fixed inlet temperature. 90% of the total kinetic energy in the mean flow is contained within the first five modes. The scatterplot for any two POD coefficients predominantly showed a circular distribution, representing a strong connection between the two modes. We speculate that the velocity and vorticity contours of spatial POD basis functions show presence of K-H instability in the flow. From DMD, eigenvalues away from the origin is observed for all the cases indicating the presence of a non-oscillatory structure. Spatial structures are also obtained from DMD. The authors are grateful to Confederation of Indian Industry and General Electric India Pvt. Ltd. for partial funding of this project.

Linear friction weld process monitoring of fixture cassette deformations using empirical mode decomposition

NASA Astrophysics Data System (ADS)

Bakker, O. J.; Gibson, C.; Wilson, P.; Lohse, N.; Popov, A. A.

2015-10-01

Due to its inherent advantages, linear friction welding is a solid-state joining process of increasing importance to the aerospace, automotive, medical and power generation equipment industries. Tangential oscillations and forge stroke during the burn-off phase of the joining process introduce essential dynamic forces, which can also be detrimental to the welding process. Since burn-off is a critical phase in the manufacturing stage, process monitoring is fundamental for quality and stability control purposes. This study aims to improve workholding stability through the analysis of fixture cassette deformations. Methods and procedures for process monitoring are developed and implemented in a fail-or-pass assessment system for fixture cassette deformations during the burn-off phase. Additionally, the de-noised signals are compared to results from previous production runs. The observed deformations as a consequence of the forces acting on the fixture cassette are measured directly during the welding process. Data on the linear friction-welding machine are acquired and de-noised using empirical mode decomposition, before the burn-off phase is extracted. This approach enables a direct, objective comparison of the signal features with trends from previous successful welds. The capacity of the whole process monitoring system is validated and demonstrated through the analysis of a large number of signals obtained from welding experiments.

Order reduction, identification and localization studies of dynamical systems

NASA Astrophysics Data System (ADS)

Ma, Xianghong

In this thesis methods are developed for performing order reduction, system identification and induction of nonlinear localization in complex mechanical dynamic systems. General techniques are proposed for constructing low-order models of linear and nonlinear mechanical systems; in addition, novel mechanical designs are considered for inducing nonlinear localization phenomena for the purpose of enhancing their dynamical performance. The thesis is in three major parts. In the first part, the transient dynamics of an impulsively loaded multi-bay truss is numerically computed by employing the Direct Global Matrix (DGM) approach. The approach is applicable to large-scale flexible structures with periodicity. Karhunen-Loeve (K-L) decomposition is used to discretize the dynamics of the truss and to create the low-order models of the truss. The leading order K-L modes are recovered by an experiment, which shows the feasibility of K-L based order reduction technique. In the second part of the thesis, nonlinear localization in dynamical systems is studied through two applications. In the seismic base isolation study, it is shown that the dynamics are sensitive to the presence of nonlinear elements and that passive motion confinement can be induced under proper design. In the coupled rod system, numerical simulation of the transient dynamics shows that a nonlinear backlash spring can induce either nonlinear localization or delocalization in the form of beat phenomena. K-L decomposition and poincare maps are utilized to study the nonlinear effects. The study shows that nonlinear localization can be induced in complex structures through backlash. In the third and final part of the thesis, a new technique based on Green!s function method is proposed to identify the dynamics of practical bolted joints. By modeling the difference between the dynamics of the bolted structure and the corresponding unbolted one, one constructs a nonparametric model for the joint dynamics. Two applications are given with a bolted beam and a truss joint in order to show the applicability of the technique.

[EMD Time-Frequency Analysis of Raman Spectrum and NIR].

PubMed

Zhao, Xiao-yu; Fang, Yi-ming; Tan, Feng; Tong, Liang; Zhai, Zhe

2016-02-01

This paper analyzes the Raman spectrum and Near Infrared Spectrum (NIR) with time-frequency method. The empirical mode decomposition spectrum becomes intrinsic mode functions, which the proportion calculation reveals the Raman spectral energy is uniform distributed in each component, while the NIR's low order intrinsic mode functions only undertakes fewer primary spectroscopic effective information. Both the real spectrum and numerical experiments show that the empirical mode decomposition (EMD) regard Raman spectrum as the amplitude-modulated signal, which possessed with high frequency adsorption property; and EMD regards NIR as the frequency-modulated signal, which could be preferably realized high frequency narrow-band demodulation during first-order intrinsic mode functions. The first-order intrinsic mode functions Hilbert transform reveals that during the period of empirical mode decomposes Raman spectrum, modal aliasing happened. Through further analysis of corn leaf's NIR in time-frequency domain, after EMD, the first and second orders components of low energy are cut off, and reconstruct spectral signal by using the remaining intrinsic mode functions, the root-mean-square error is 1.001 1, and the correlation coefficient is 0.981 3, both of these two indexes indicated higher accuracy in re-construction; the decomposition trend term indicates the absorbency is ascending along with the decreasing to wave length in the near-infrared light wave band; and the Hilbert transform of characteristic modal component displays, 657 cm⁻¹ is the specific frequency by the corn leaf stress spectrum, which could be regarded as characteristic frequency for identification.

Response of rotation-translation blocked proteins using Langevin dynamics on a locally harmonic landscape.

PubMed

Manson, Anthony C; Coalson, Rob D

2012-10-11

Langevin dynamics is used to compute the time evolution of the nonequilibrium motion of the atomic coordinates of a protein in response to ligand dissociation. The protein potential energy surface (PES) is approximated by a harmonic basin about the minimum of the unliganded state. Upon ligand dissociation, the protein undergoes relaxation from the bound to the unbound state. A coarse graining scheme based on rotation translation blocks (RTB) is applied to the relaxation of the two domain iron transport protein, ferric binding protein. This scheme provides a natural and efficient way to freeze out the small amplitude, high frequency motions within each rigid fragment, thereby allowing for the number of dynamical degrees of freedom to be reduced. The results obtained from all flexible atom (constraint free) dynamics are compared to those obtained using RTB-Langevin dynamics. To assess the impact of the assumed rigid fragment clustering on the temporal relaxation dynamics of the protein molecule, three distinct rigid block decompositions were generated and their responses compared. Each of the decompositions was a variant of the one-block-per-residue grouping, with their force and friction matrices being derived from their fully flexible counterpart. Monitoring the time evolution of the distance separating a selected pair of amino acids, the response curves of the blocked decompositions were similar in shape to each other and to the control system in which all atomic degrees of freedom are fully independent. The similar shape of the blocked responses showed that the variations in grouping had only a minor impact on the kinematics. Compared with the all atom responses, however, the blocked responses were faster as a result of the instantaneous transmission of force throughout each rigid block. This occurred because rigid blocking does not permit any intrablock deformation that could store or divert energy. It was found, however, that this accelerated response could be successfully corrected by scaling each eigenvalue in the appropriate propagation matrix by the least-squares fitted slope of the blocked vs nonblocked eigenvalue spectra. The RTB responses for each test system were dominated by small eigenvalue overdamped Langevin modes. The large eigenvalue members of each response dissipated within the first 5 ps, after which the long time response was dominated by a modest set of low energy, overdamped normal modes, that were characterized by highly cooperative, functionally relevant displacements. The response assuming that the system is in the overdamped limit was compared to the full phase space Langevin dynamics results. The responses after the first 5 ps were nearly identical, confirming that the inertial components were significant only in the initial stages of the relaxation. Since the propagator matrix in the overdamped formulation is real-symmetric and does not require the inertial component in the propagator, the computation time and memory footprint was reduced by 1 order of magnitude.

Temporal dynamics of phosphorus during aquatic and terrestrial litter decomposition in an alpine forest.

PubMed

Peng, Yan; Yang, Wanqin; Yue, Kai; Tan, Bo; Huang, Chunping; Xu, Zhenfeng; Ni, Xiangyin; Zhang, Li; Wu, Fuzhong

2018-06-17

Plant litter decomposition in forested soil and watershed is an important source of phosphorus (P) for plants in forest ecosystems. Understanding P dynamics during litter decomposition in forested aquatic and terrestrial ecosystems will be of great importance for better understanding nutrient cycling across forest landscape. However, despite massive studies addressing litter decomposition have been carried out, generalizations across aquatic and terrestrial ecosystems regarding the temporal dynamics of P loss during litter decomposition remain elusive. We conducted a two-year field experiment using litterbag method in both aquatic (streams and riparian zones) and terrestrial (forest floors) ecosystems in an alpine forest on the eastern Tibetan Plateau. By using multigroup comparisons of structural equation modeling (SEM) method with different litter mass-loss intervals, we explicitly assessed the direct and indirect effects of several biotic and abiotic drivers on P loss across different decomposition stages. The results suggested that (1) P concentration in decomposing litter showed similar patterns of early increase and later decrease across different species and ecosystems types; (2) P loss shared a common hierarchy of drivers across different ecosystems types, with litter chemical dynamics mainly having direct effects but environment and initial litter quality having both direct and indirect effects; (3) when assessing at the temporal scale, the effects of initial litter quality appeared to increase in late decomposition stages, while litter chemical dynamics showed consistent significant effects almost in all decomposition stages across aquatic and terrestrial ecosystems; (4) microbial diversity showed significant effects on P loss, but its effects were lower compared with other drivers. Our results highlight the importance of including spatiotemporal variations and indicate the possibility of integrating aquatic and terrestrial decomposition into a common framework for future construction of models that account for the temporal dynamics of P in decomposing litter. Copyright © 2018 Elsevier B.V. All rights reserved.

Users manual for the Variable dimension Automatic Synthesis Program (VASP)

NASA Technical Reports Server (NTRS)

White, J. S.; Lee, H. Q.

1971-01-01

A dictionary and some problems for the Variable Automatic Synthesis Program VASP are submitted. The dictionary contains a description of each subroutine and instructions on its use. The example problems give the user a better perspective on the use of VASP for solving problems in modern control theory. These example problems include dynamic response, optimal control gain, solution of the sampled data matrix Ricatti equation, matrix decomposition, and pseudo inverse of a matrix. Listings of all subroutines are also included. The VASP program has been adapted to run in the conversational mode on the Ames 360/67 computer.

IR spectral assignments for the hydrated excess proton in liquid water.

PubMed

Biswas, Rajib; Carpenter, William; Fournier, Joseph A; Voth, Gregory A; Tokmakoff, Andrei

2017-04-21

The local environmental sensitivity of infrared (IR) spectroscopy to a hydrogen-bonding structure makes it a powerful tool for investigating the structure and dynamics of excess protons in water. Although of significant interest, the line broadening that results from the ultrafast evolution of different solvated proton-water structures makes the assignment of liquid-phase IR spectra a challenging task. In this work, we apply a normal mode analysis using density functional theory of thousands of proton-water clusters taken from reactive molecular dynamics trajectories of the latest generation multistate empirical valence bond proton model (MS-EVB 3.2). These calculations are used to obtain a vibrational density of states and IR spectral density, which are decomposed on the basis of solvated proton structure and the frequency dependent mode character. Decompositions are presented on the basis of the proton sharing parameter δ, often used to distinguish Eigen and Zundel species, the stretch and bend character of the modes, the mode delocalization, and the vibrational mode symmetry. We find there is a wide distribution of vibrational frequencies spanning 1200-3000 cm -1 for every local proton configuration, with the region 2000-2600 cm -1 being mostly governed by the distorted Eigen-like configuration. We find a continuous red shift of the special-pair O⋯H + ⋯O stretching frequency, and an increase in the flanking water bending intensity with decreasing δ. Also, we find that the flanking water stretch mode of the Zundel-like species is strongly mixed with the flanking water bend, and the special pair proton oscillation band is strongly coupled with the bend modes of the central H 5 O2+moiety.

IR spectral assignments for the hydrated excess proton in liquid water

NASA Astrophysics Data System (ADS)

Biswas, Rajib; Carpenter, William; Fournier, Joseph A.; Voth, Gregory A.; Tokmakoff, Andrei

2017-04-01

The local environmental sensitivity of infrared (IR) spectroscopy to a hydrogen-bonding structure makes it a powerful tool for investigating the structure and dynamics of excess protons in water. Although of significant interest, the line broadening that results from the ultrafast evolution of different solvated proton-water structures makes the assignment of liquid-phase IR spectra a challenging task. In this work, we apply a normal mode analysis using density functional theory of thousands of proton-water clusters taken from reactive molecular dynamics trajectories of the latest generation multistate empirical valence bond proton model (MS-EVB 3.2). These calculations are used to obtain a vibrational density of states and IR spectral density, which are decomposed on the basis of solvated proton structure and the frequency dependent mode character. Decompositions are presented on the basis of the proton sharing parameter δ, often used to distinguish Eigen and Zundel species, the stretch and bend character of the modes, the mode delocalization, and the vibrational mode symmetry. We find there is a wide distribution of vibrational frequencies spanning 1200-3000 cm-1 for every local proton configuration, with the region 2000-2600 cm-1 being mostly governed by the distorted Eigen-like configuration. We find a continuous red shift of the special-pair O⋯H+⋯O stretching frequency, and an increase in the flanking water bending intensity with decreasing δ. Also, we find that the flanking water stretch mode of the Zundel-like species is strongly mixed with the flanking water bend, and the special pair proton oscillation band is strongly coupled with the bend modes of the central H5+O2 moiety.

Multi-Fault Diagnosis of Rolling Bearings via Adaptive Projection Intrinsically Transformed Multivariate Empirical Mode Decomposition and High Order Singular Value Decomposition

PubMed Central

Lv, Yong; Song, Gangbing

2018-01-01

Rolling bearings are important components in rotary machinery systems. In the field of multi-fault diagnosis of rolling bearings, the vibration signal collected from single channels tends to miss some fault characteristic information. Using multiple sensors to collect signals at different locations on the machine to obtain multivariate signal can remedy this problem. The adverse effect of a power imbalance between the various channels is inevitable, and unfavorable for multivariate signal processing. As a useful, multivariate signal processing method, Adaptive-projection has intrinsically transformed multivariate empirical mode decomposition (APIT-MEMD), and exhibits better performance than MEMD by adopting adaptive projection strategy in order to alleviate power imbalances. The filter bank properties of APIT-MEMD are also adopted to enable more accurate and stable intrinsic mode functions (IMFs), and to ease mode mixing problems in multi-fault frequency extractions. By aligning IMF sets into a third order tensor, high order singular value decomposition (HOSVD) can be employed to estimate the fault number. The fault correlation factor (FCF) analysis is used to conduct correlation analysis, in order to determine effective IMFs; the characteristic frequencies of multi-faults can then be extracted. Numerical simulations and the application of multi-fault situation can demonstrate that the proposed method is promising in multi-fault diagnoses of multivariate rolling bearing signal. PMID:29659510

Multi-Fault Diagnosis of Rolling Bearings via Adaptive Projection Intrinsically Transformed Multivariate Empirical Mode Decomposition and High Order Singular Value Decomposition.

PubMed

Yuan, Rui; Lv, Yong; Song, Gangbing

2018-04-16

Rolling bearings are important components in rotary machinery systems. In the field of multi-fault diagnosis of rolling bearings, the vibration signal collected from single channels tends to miss some fault characteristic information. Using multiple sensors to collect signals at different locations on the machine to obtain multivariate signal can remedy this problem. The adverse effect of a power imbalance between the various channels is inevitable, and unfavorable for multivariate signal processing. As a useful, multivariate signal processing method, Adaptive-projection has intrinsically transformed multivariate empirical mode decomposition (APIT-MEMD), and exhibits better performance than MEMD by adopting adaptive projection strategy in order to alleviate power imbalances. The filter bank properties of APIT-MEMD are also adopted to enable more accurate and stable intrinsic mode functions (IMFs), and to ease mode mixing problems in multi-fault frequency extractions. By aligning IMF sets into a third order tensor, high order singular value decomposition (HOSVD) can be employed to estimate the fault number. The fault correlation factor (FCF) analysis is used to conduct correlation analysis, in order to determine effective IMFs; the characteristic frequencies of multi-faults can then be extracted. Numerical simulations and the application of multi-fault situation can demonstrate that the proposed method is promising in multi-fault diagnoses of multivariate rolling bearing signal.

Data-based adjoint and H2 optimal control of the Ginzburg-Landau equation

NASA Astrophysics Data System (ADS)

Banks, Michael; Bodony, Daniel

2017-11-01

Equation-free, reduced-order methods of control are desirable when the governing system of interest is of very high dimension or the control is to be applied to a physical experiment. Two-phase flow optimal control problems, our target application, fit these criteria. Dynamic Mode Decomposition (DMD) is a data-driven method for model reduction that can be used to resolve the dynamics of very high dimensional systems and project the dynamics onto a smaller, more manageable basis. We evaluate the effectiveness of DMD-based forward and adjoint operator estimation when applied to H2 optimal control approaches applied to the linear and nonlinear Ginzburg-Landau equation. Perspectives on applying the data-driven adjoint to two phase flow control will be given. Office of Naval Research (ONR) as part of the Multidisciplinary University Research Initiatives (MURI) Program, under Grant Number N00014-16-1-2617.

INDDGO: Integrated Network Decomposition & Dynamic programming for Graph Optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Groer, Christopher S; Sullivan, Blair D; Weerapurage, Dinesh P

2012-10-01

It is well-known that dynamic programming algorithms can utilize tree decompositions to provide a way to solve some \\emph{NP}-hard problems on graphs where the complexity is polynomial in the number of nodes and edges in the graph, but exponential in the width of the underlying tree decomposition. However, there has been relatively little computational work done to determine the practical utility of such dynamic programming algorithms. We have developed software to construct tree decompositions using various heuristics and have created a fast, memory-efficient dynamic programming implementation for solving maximum weighted independent set. We describe our software and the algorithms wemore » have implemented, focusing on memory saving techniques for the dynamic programming. We compare the running time and memory usage of our implementation with other techniques for solving maximum weighted independent set, including a commercial integer programming solver and a semi-definite programming solver. Our results indicate that it is possible to solve some instances where the underlying decomposition has width much larger than suggested by the literature. For certain types of problems, our dynamic programming code runs several times faster than these other methods.« less

A tripolar pattern as an internal mode of the East Asian summer monsoon

NASA Astrophysics Data System (ADS)

Hirota, Nagio; Takahashi, Masaaki

2012-11-01

A tripolar anomaly pattern with centers located around the Philippines, China/Japan, and East Siberia dominantly appears in climate variations of the East Asian summer monsoon. In this study, we extracted this pattern as the first mode of a singular value decomposition (SVD1) over East Asia. The squared covariance fraction of SVD1 was 59 %, indicating that this pattern can be considered a dominant pattern of climate variations. Moreover, the results of numerical experiments suggested that the structure is also a dominant pattern of linear responses, even if external forcing is distributed homogeneously over the Northern Hemisphere. Thus, the tripolar pattern can be considered an internal mode that is characterized by the internal atmospheric processes. In this pattern, the moist processes strengthen the circulation anomalies, the dynamical energy conversion supplies energy to the anomalies, and the Rossby waves propagate northward in the lower troposphere and southeastward in the upper troposphere. These processes are favorable for the pattern to have large amplitude and to influence a large area.

Pi2 detection using Empirical Mode Decomposition (EMD)

NASA Astrophysics Data System (ADS)

Mieth, Johannes Z. D.; Frühauff, Dennis; Glassmeier, Karl-Heinz

2017-04-01

Empirical Mode Decomposition has been used as an alternative method to wavelet transformation to identify onset times of Pi2 pulsations in data sets of the Scandinavian Magnetometer Array (SMA). Pi2 pulsations are magnetohydrodynamic waves occurring during magnetospheric substorms. Almost always Pi2 are observed at substorm onset in mid to low latitudes on Earth's nightside. They are fed by magnetic energy release caused by dipolarization processes. Their periods lie between 40 to 150 seconds. Usually, Pi2 are detected using wavelet transformation. Here, Empirical Mode Decomposition (EMD) is presented as an alternative approach to the traditional procedure. EMD is a young signal decomposition method designed for nonlinear and non-stationary time series. It provides an adaptive, data driven, and complete decomposition of time series into slow and fast oscillations. An optimized version using Monte-Carlo-type noise assistance is used here. By displaying the results in a time-frequency space a characteristic frequency modulation is observed. This frequency modulation can be correlated with the onset of Pi2 pulsations. A basic algorithm to find the onset is presented. Finally, the results are compared to classical wavelet-based analysis. The use of different SMA stations furthermore allows the spatial analysis of Pi2 onset times. EMD mostly finds application in the fields of engineering and medicine. This work demonstrates the applicability of this method to geomagnetic time series.

Low Dimensional Analysis of Wing Surface Morphology in Hummingbird Free Flight

NASA Astrophysics Data System (ADS)

Shallcross, Gregory; Ren, Yan; Liu, Geng; Dong, Haibo; Tobalske, Bret

2015-11-01

Surface morphing in flapping wings is a hallmark of bird flight. In current work, the role of dynamic wing morphing of a free flying hummingbird is studied in detail. A 3D image-based surface reconstruction method is used to obtain the kinematics and deformation of hummingbird wings from high-quality high-speed videos. The observed wing surface morphing is highly complex and a number of modeling methods including singular value decomposition (SVD) are used to obtain the fundamental kinematical modes with distinct motion features. Their aerodynamic roles are investigated by conducting immersed-boundary-method based flow simulations. The results show that the chord-wise deformation modes play key roles in the attachment of leading-edge vortex, thus improve the performance of the flapping wings. This work is supported by NSF CBET-1313217 and AFOSR FA9550-12-1-0071.

Boreal soil carbon dynamics under a changing climate: a model inversion approach

Treesearch

Zhaosheng Fan; Jason C. Neff; Jennifer W. Harden; Kimberly P. Wickland

2008-01-01

Several fundamental but important factors controlling the feedback of boreal organic carbon (OC) to climate change were examined using a mechanistic model of soil OC dynamics, including the combined effects of temperature and moisture on the decomposition of OC and the factors controlling carbon quality and decomposition with depth. To estimate decomposition rates and...

Computer implemented empirical mode decomposition method, apparatus and article of manufacture

NASA Technical Reports Server (NTRS)

Huang, Norden E. (Inventor)

1999-01-01

A computer implemented physical signal analysis method is invented. This method includes two essential steps and the associated presentation techniques of the results. All the steps exist only in a computer: there are no analytic expressions resulting from the method. The first step is a computer implemented Empirical Mode Decomposition to extract a collection of Intrinsic Mode Functions (IMF) from nonlinear, nonstationary physical signals. The decomposition is based on the direct extraction of the energy associated with various intrinsic time scales in the physical signal. Expressed in the IMF's, they have well-behaved Hilbert Transforms from which instantaneous frequencies can be calculated. The second step is the Hilbert Transform. The final result is the Hilbert Spectrum. Thus, the invention can localize any event on the time as well as the frequency axis. The decomposition can also be viewed as an expansion of the data in terms of the IMF's. Then, these IMF's, based on and derived from the data, can serve as the basis of that expansion. The local energy and the instantaneous frequency derived from the IMF's through the Hilbert transform give a full energy-frequency-time distribution of the data which is designated as the Hilbert Spectrum.

Iterative filtering decomposition based on local spectral evolution kernel

PubMed Central

Wang, Yang; Wei, Guo-Wei; Yang, Siyang

2011-01-01

The synthesizing information, achieving understanding, and deriving insight from increasingly massive, time-varying, noisy and possibly conflicting data sets are some of most challenging tasks in the present information age. Traditional technologies, such as Fourier transform and wavelet multi-resolution analysis, are inadequate to handle all of the above-mentioned tasks. The empirical model decomposition (EMD) has emerged as a new powerful tool for resolving many challenging problems in data processing and analysis. Recently, an iterative filtering decomposition (IFD) has been introduced to address the stability and efficiency problems of the EMD. Another data analysis technique is the local spectral evolution kernel (LSEK), which provides a near prefect low pass filter with desirable time-frequency localizations. The present work utilizes the LSEK to further stabilize the IFD, and offers an efficient, flexible and robust scheme for information extraction, complexity reduction, and signal and image understanding. The performance of the present LSEK based IFD is intensively validated over a wide range of data processing tasks, including mode decomposition, analysis of time-varying data, information extraction from nonlinear dynamic systems, etc. The utility, robustness and usefulness of the proposed LESK based IFD are demonstrated via a large number of applications, such as the analysis of stock market data, the decomposition of ocean wave magnitudes, the understanding of physiologic signals and information recovery from noisy images. The performance of the proposed method is compared with that of existing methods in the literature. Our results indicate that the LSEK based IFD improves both the efficiency and the stability of conventional EMD algorithms. PMID:22350559

Soil Properties, Nutrient Dynamics, and Soil Enzyme Activities Associated with Garlic Stalk Decomposition under Various Conditions

PubMed Central

Han, Xu; Cheng, Zhihui; Meng, Huanwen

2012-01-01

The garlic stalk is a byproduct of garlic production and normally abandoned or burned, both of which cause environmental pollution. It is therefore appropriate to determine the conditions of efficient decomposition, and equally appropriate to determine the impact of this decomposition on soil properties. In this study, the soil properties, enzyme activities and nutrient dynamics associated with the decomposition of garlic stalk at different temperatures, concentrations and durations were investigated. Stalk decomposition significantly increased the values of soil pH and electrical conductivity. In addition, total nitrogen and organic carbon concentration were significantly increased by decomposing stalks at 40°C, with a 5∶100 ratio and for 10 or 60 days. The highest activities of sucrase, urease and alkaline phosphatase in soil were detected when stalk decomposition was performed at the lowest temperature (10°C), highest concentration (5∶100), and shortest duration (10 or 20 days). The evidence presented here suggests that garlic stalk decomposition improves the quality of soil by altering the value of soil pH and electrical conductivity and by changing nutrient dynamics and soil enzyme activity, compared to the soil decomposition without garlic stalks. PMID:23226411

Coherent mode decomposition using mixed Wigner functions of Hermite-Gaussian beams.

PubMed

Tanaka, Takashi

2017-04-15

A new method of coherent mode decomposition (CMD) is proposed that is based on a Wigner-function representation of Hermite-Gaussian beams. In contrast to the well-known method using the cross spectral density (CSD), it directly determines the mode functions and their weights without solving the eigenvalue problem. This facilitates the CMD of partially coherent light whose Wigner functions (and thus CSDs) are not separable, in which case the conventional CMD requires solving an eigenvalue problem with a large matrix and thus is numerically formidable. An example is shown regarding the CMD of synchrotron radiation, one of the most important applications of the proposed method.

Eliminating the zero spectrum in Fourier transform profilometry using empirical mode decomposition.

PubMed

Li, Sikun; Su, Xianyu; Chen, Wenjing; Xiang, Liqun

2009-05-01

Empirical mode decomposition is introduced into Fourier transform profilometry to extract the zero spectrum included in the deformed fringe pattern without the need for capturing two fringe patterns with pi phase difference. The fringe pattern is subsequently demodulated using a standard Fourier transform profilometry algorithm. With this method, the deformed fringe pattern is adaptively decomposed into a finite number of intrinsic mode functions that vary from high frequency to low frequency by means of an algorithm referred to as a sifting process. Then the zero spectrum is separated from the high-frequency components effectively. Experiments validate the feasibility of this method.

Synchronization in networks with heterogeneous coupling delays

NASA Astrophysics Data System (ADS)

Otto, Andreas; Radons, Günter; Bachrathy, Dániel; Orosz, Gábor

2018-01-01

Synchronization in networks of identical oscillators with heterogeneous coupling delays is studied. A decomposition of the network dynamics is obtained by block diagonalizing a newly introduced adjacency lag operator which contains the topology of the network as well as the corresponding coupling delays. This generalizes the master stability function approach, which was developed for homogenous delays. As a result the network dynamics can be analyzed by delay differential equations with distributed delay, where different delay distributions emerge for different network modes. Frequency domain methods are used for the stability analysis of synchronized equilibria and synchronized periodic orbits. As an example, the synchronization behavior in a system of delay-coupled Hodgkin-Huxley neurons is investigated. It is shown that the parameter regions where synchronized periodic spiking is unstable expand when increasing the delay heterogeneity.

Extreme learning machine for reduced order modeling of turbulent geophysical flows.

PubMed

San, Omer; Maulik, Romit

2018-04-01

We investigate the application of artificial neural networks to stabilize proper orthogonal decomposition-based reduced order models for quasistationary geophysical turbulent flows. An extreme learning machine concept is introduced for computing an eddy-viscosity closure dynamically to incorporate the effects of the truncated modes. We consider a four-gyre wind-driven ocean circulation problem as our prototype setting to assess the performance of the proposed data-driven approach. Our framework provides a significant reduction in computational time and effectively retains the dynamics of the full-order model during the forward simulation period beyond the training data set. Furthermore, we show that the method is robust for larger choices of time steps and can be used as an efficient and reliable tool for long time integration of general circulation models.

Extreme learning machine for reduced order modeling of turbulent geophysical flows

NASA Astrophysics Data System (ADS)

San, Omer; Maulik, Romit

2018-04-01

We investigate the application of artificial neural networks to stabilize proper orthogonal decomposition-based reduced order models for quasistationary geophysical turbulent flows. An extreme learning machine concept is introduced for computing an eddy-viscosity closure dynamically to incorporate the effects of the truncated modes. We consider a four-gyre wind-driven ocean circulation problem as our prototype setting to assess the performance of the proposed data-driven approach. Our framework provides a significant reduction in computational time and effectively retains the dynamics of the full-order model during the forward simulation period beyond the training data set. Furthermore, we show that the method is robust for larger choices of time steps and can be used as an efficient and reliable tool for long time integration of general circulation models.

Integrated Physics-based Modeling and Experiments for Improved Prediction of Combustion Dynamics in Low-Emission Systems

NASA Technical Reports Server (NTRS)

Anderson, William E.; Lucht, Robert P.; Mongia, Hukam

2015-01-01

Concurrent simulation and experiment was undertaken to assess the ability of a hybrid RANS-LES model to predict combustion dynamics in a single-element lean direct-inject (LDI) combustor showing self-excited instabilities. High frequency pressure modes produced by Fourier and modal decomposition analysis were compared quantitatively, and trends with equivalence ratio and inlet temperature were compared qualitatively. High frequency OH PLIF and PIV measurements were also taken. Submodels for chemical kinetics and primary and secondary atomization were also tested against the measured behavior. For a point-wise comparison, the amplitudes matched within a factor of two. The dependence on equivalence ratio was matched. Preliminary results from simulation using an 18-reaction kinetics model indicated instability amplitudes closer to measurement. Analysis of the simulations suggested a band of modes around 1400 Hz were due to a vortex bubble breakdown and a band of modes around 6 kHz were due to a precessing vortex core hydrodynamic instability. The primary needs are directly coupled and validated ab initio models of the atomizer free surface flow and the primary atomization processes, and more detailed study of the coupling between the 3D swirling flow and the local thermoacoustics in the diverging venturi section.

Data-driven Climate Modeling and Prediction

NASA Astrophysics Data System (ADS)

Kondrashov, D. A.; Chekroun, M.

2016-12-01

Global climate models aim to simulate a broad range of spatio-temporal scales of climate variability with state vector having many millions of degrees of freedom. On the other hand, while detailed weather prediction out to a few days requires high numerical resolution, it is fairly clear that a major fraction of large-scale climate variability can be predicted in a much lower-dimensional phase space. Low-dimensional models can simulate and predict this fraction of climate variability, provided they are able to account for linear and nonlinear interactions between the modes representing large scales of climate dynamics, as well as their interactions with a much larger number of modes representing fast and small scales. This presentation will highlight several new applications by Multilayered Stochastic Modeling (MSM) [Kondrashov, Chekroun and Ghil, 2015] framework that has abundantly proven its efficiency in the modeling and real-time forecasting of various climate phenomena. MSM is a data-driven inverse modeling technique that aims to obtain a low-order nonlinear system of prognostic equations driven by stochastic forcing, and estimates both the dynamical operator and the properties of the driving noise from multivariate time series of observations or a high-end model's simulation. MSM leads to a system of stochastic differential equations (SDEs) involving hidden (auxiliary) variables of fast-small scales ranked by layers, which interact with the macroscopic (observed) variables of large-slow scales to model the dynamics of the latter, and thus convey memory effects. New MSM climate applications focus on development of computationally efficient low-order models by using data-adaptive decomposition methods that convey memory effects by time-embedding techniques, such as Multichannel Singular Spectrum Analysis (M-SSA) [Ghil et al. 2002] and recently developed Data-Adaptive Harmonic (DAH) decomposition method [Chekroun and Kondrashov, 2016]. In particular, new results by DAH-MSM modeling and prediction of Arctic Sea Ice, as well as decadal predictions of near-surface Earth temperatures will be presented.

Generalized decompositions of dynamic systems and vector Lyapunov functions

NASA Astrophysics Data System (ADS)

Ikeda, M.; Siljak, D. D.

1981-10-01

The notion of decomposition is generalized to provide more freedom in constructing vector Lyapunov functions for stability analysis of nonlinear dynamic systems. A generalized decomposition is defined as a disjoint decomposition of a system which is obtained by expanding the state-space of a given system. An inclusion principle is formulated for the solutions of the expansion to include the solutions of the original system, so that stability of the expansion implies stability of the original system. Stability of the expansion can then be established by standard disjoint decompositions and vector Lyapunov functions. The applicability of the new approach is demonstrated using the Lotka-Volterra equations.

On Convergence of Extended Dynamic Mode Decomposition to the Koopman Operator

NASA Astrophysics Data System (ADS)

Korda, Milan; Mezić, Igor

2018-04-01

Extended dynamic mode decomposition (EDMD) (Williams et al. in J Nonlinear Sci 25(6):1307-1346, 2015) is an algorithm that approximates the action of the Koopman operator on an N-dimensional subspace of the space of observables by sampling at M points in the state space. Assuming that the samples are drawn either independently or ergodically from some measure μ , it was shown in Klus et al. (J Comput Dyn 3(1):51-79, 2016) that, in the limit as M→ ∞, the EDMD operator K_{N,M} converges to K_N, where K_N is the L_2(μ )-orthogonal projection of the action of the Koopman operator on the finite-dimensional subspace of observables. We show that, as N → ∞, the operator K_N converges in the strong operator topology to the Koopman operator. This in particular implies convergence of the predictions of future values of a given observable over any finite time horizon, a fact important for practical applications such as forecasting, estimation and control. In addition, we show that accumulation points of the spectra of K_N correspond to the eigenvalues of the Koopman operator with the associated eigenfunctions converging weakly to an eigenfunction of the Koopman operator, provided that the weak limit of the eigenfunctions is nonzero. As a by-product, we propose an analytic version of the EDMD algorithm which, under some assumptions, allows one to construct K_N directly, without the use of sampling. Finally, under additional assumptions, we analyze convergence of K_{N,N} (i.e., M=N), proving convergence, along a subsequence, to weak eigenfunctions (or eigendistributions) related to the eigenmeasures of the Perron-Frobenius operator. No assumptions on the observables belonging to a finite-dimensional invariant subspace of the Koopman operator are required throughout.

Interdependence between Greece and other European stock markets: A comparison of wavelet and VMD copula, and the portfolio implications

NASA Astrophysics Data System (ADS)

Shahzad, Syed Jawad Hussain; Kumar, Ronald Ravinesh; Ali, Sajid; Ameer, Saba

2016-09-01

The interdependence of Greece and other European stock markets and the subsequent portfolio implications are examined in wavelet and variational mode decomposition domain. In applying the decomposition techniques, we analyze the structural properties of data and distinguish between short and long term dynamics of stock market returns. First, the GARCH-type models are fitted to obtain the standardized residuals. Next, different copula functions are evaluated, and based on the conventional information criteria and time varying parameter, Joe-Clayton copula is chosen to model the tail dependence between the stock markets. The short-run lower tail dependence time paths show a sudden increase in comovement during the global financial crises. The results of the long-run dependence suggest that European stock markets have higher interdependence with Greece stock market. Individual country's Value at Risk (VaR) separates the countries into two distinct groups. Finally, the two-asset portfolio VaR measures provide potential markets for Greece stock market investment diversification.

Low-dimensional and Data Fusion Techniques Applied to a Rectangular Supersonic Multi-stream Jet

NASA Astrophysics Data System (ADS)

Berry, Matthew; Stack, Cory; Magstadt, Andrew; Ali, Mohd; Gaitonde, Datta; Glauser, Mark

2017-11-01

Low-dimensional models of experimental and simulation data for a complex supersonic jet were fused to reconstruct time-dependent proper orthogonal decomposition (POD) coefficients. The jet consists of a multi-stream rectangular single expansion ramp nozzle, containing a core stream operating at Mj , 1 = 1.6 , and bypass stream at Mj , 3 = 1.0 with an underlying deck. POD was applied to schlieren and PIV data to acquire the spatial basis functions. These eigenfunctions were projected onto their corresponding time-dependent large eddy simulation (LES) fields to reconstruct the temporal POD coefficients. This reconstruction was able to resolve spectral peaks that were previously aliased due to the slower sampling rates of the experiments. Additionally, dynamic mode decomposition (DMD) was applied to the experimental and LES datasets, and the spatio-temporal characteristics were compared to POD. The authors would like to acknowledge AFOSR, program manager Dr. Doug Smith, for funding this research, Grant No. FA9550-15-1-0435.

Operational modal analysis using SVD of power spectral density transmissibility matrices

NASA Astrophysics Data System (ADS)

Araújo, Iván Gómez; Laier, Jose Elias

2014-05-01

This paper proposes the singular value decomposition of power spectrum density transmissibility matrices with different references, (PSDTM-SVD), as an identification method of natural frequencies and mode shapes of a dynamic system subjected to excitations under operational conditions. At the system poles, the rows of the proposed transmissibility matrix converge to the same ratio of amplitudes of vibration modes. As a result, the matrices are linearly dependent on the columns, and their singular values converge to zero. Singular values are used to determine the natural frequencies, and the first left singular vectors are used to estimate mode shapes. A numerical example of the finite element model of a beam subjected to colored noise excitation is analyzed to illustrate the accuracy of the proposed method. Results of the PSDTM-SVD method in the numerical example are compared with obtained using frequency domain decomposition (FDD) and power spectrum density transmissibility (PSDT). It is demonstrated that the proposed method does not depend on the excitation characteristics contrary to the FDD method that assumes white noise excitation, and further reduces the risk to identify extra non-physical poles in comparison to the PSDT method. Furthermore, a case study is performed using data from an operational vibration test of a bridge with a simply supported beam system. The real application of a full-sized bridge has shown that the proposed PSDTM-SVD method is able to identify the operational modal parameter. Operational modal parameters identified by the PSDTM-SVD in the real application agree well those identified by the FDD and PSDT methods.

High-speed imaging of submerged jet: visualization analysis using proper orthogonality decomposition

NASA Astrophysics Data System (ADS)

Liu, Yingzheng; He, Chuangxin

2016-11-01

In the present study, the submerged jet at low Reynolds numbers was visualized using laser induced fluoresce and high-speed imaging in a water tank. Well-controlled calibration was made to determine linear dependency region of the fluoresce intensity on its concentration. Subsequently, the jet fluid issuing from a circular pipe was visualized using a high-speed camera. The animation sequence of the visualized jet flow field was supplied for the snapshot proper orthogonality decomposition (POD) analysis. Spatio-temporally varying structures superimposed in the unsteady fluid flow were identified, e.g., the axisymmetric mode and the helical mode, which were reflected from the dominant POD modes. The coefficients of the POD modes give strong indication of temporal and spectral features of the corresponding unsteady events. The reconstruction using the time-mean visualization and the selected POD modes was conducted to reveal the convective motion of the buried vortical structures. National Natural Science Foundation of China.

Displacement prediction of Baijiabao landslide based on empirical mode decomposition and long short-term memory neural network in Three Gorges area, China

NASA Astrophysics Data System (ADS)

Xu, Shiluo; Niu, Ruiqing

2018-02-01

Every year, landslides pose huge threats to thousands of people in China, especially those in the Three Gorges area. It is thus necessary to establish an early warning system to help prevent property damage and save peoples' lives. Most of the landslide displacement prediction models that have been proposed are static models. However, landslides are dynamic systems. In this paper, the total accumulative displacement of the Baijiabao landslide is divided into trend and periodic components using empirical mode decomposition. The trend component is predicted using an S-curve estimation, and the total periodic component is predicted using a long short-term memory neural network (LSTM). LSTM is a dynamic model that can remember historical information and apply it to the current output. Six triggering factors are chosen to predict the periodic term using the Pearson cross-correlation coefficient and mutual information. These factors include the cumulative precipitation during the previous month, the cumulative precipitation during a two-month period, the reservoir level during the current month, the change in the reservoir level during the previous month, the cumulative increment of the reservoir level during the current month, and the cumulative displacement during the previous month. When using one-step-ahead prediction, LSTM yields a root mean squared error (RMSE) value of 6.112 mm, while the support vector machine for regression (SVR) and the back-propagation neural network (BP) yield values of 10.686 mm and 8.237 mm, respectively. Meanwhile, the Elman network (Elman) yields an RMSE value of 6.579 mm. In addition, when using multi-step-ahead prediction, LSTM obtains an RMSE value of 8.648 mm, while SVR, BP and the Elman network obtains RSME values of 13.418 mm, 13.014 mm, and 13.370 mm. The predicted results indicate that, to some extent, the dynamic model (LSTM) achieves results that are more accurate than those of the static models (i.e., SVR and BP). LSTM even displays better performance than the Elman network, which is also a dynamic method.

Effects of prescribed burning and litter type on litter decomposition and nutrient release in mixed-grass prairie in Eastern Montana

USDA-ARS?s Scientific Manuscript database

Fire can affect litter decomposition and carbon (C) and nitrogen (N) dynamics. Here, we examined the effect of summer fire and three litter types on litter decomposition and litter C and N dynamics in a northern mixed-grass prairie over a 24 month period starting ca. 14 months after fire. Over all...

A spatial picture of the synthetic large-scale motion from dynamic roughness

NASA Astrophysics Data System (ADS)

Huynh, David; McKeon, Beverley

2017-11-01

Jacobi and McKeon (2011) set up a dynamic roughness apparatus to excite a synthetic, travelling wave-like disturbance in a wind tunnel, boundary layer study. In the present work, this dynamic roughness has been adapted for a flat-plate, turbulent boundary layer experiment in a water tunnel. A key advantage of operating in water as opposed to air is the longer flow timescales. This makes accessible higher non-dimensional actuation frequencies and correspondingly shorter synthetic length scales, and is thus more amenable to particle image velocimetry. As a result, this experiment provides a novel spatial picture of the synthetic mode, the coupled small scales, and their streamwise development. It is demonstrated that varying the roughness actuation frequency allows for significant tuning of the streamwise wavelength of the synthetic mode, with a range of 3 δ-13 δ being achieved. Employing a phase-locked decomposition, spatial snapshots are constructed of the synthetic large scale and used to analyze its streamwise behavior. Direct spatial filtering is used to separate the synthetic large scale and the related small scales, and the results are compared to those obtained by temporal filtering that invokes Taylor's hypothesis. The support of AFOSR (Grant # FA9550-16-1-0361) is gratefully acknowledged.

The processing of aluminum gasarites via thermal decomposition of interstitial hydrides

NASA Astrophysics Data System (ADS)

Licavoli, Joseph J.

Gasarite structures are a unique type of metallic foam containing tubular pores. The original methods for their production limited them to laboratory study despite appealing foam properties. Thermal decomposition processing of gasarites holds the potential to increase the application of gasarite foams in engineering design by removing several barriers to their industrial scale production. The following study characterized thermal decomposition gasarite processing both experimentally and theoretically. It was found that significant variation was inherent to this process therefore several modifications were necessary to produce gasarites using this method. Conventional means to increase porosity and enhance pore morphology were studied. Pore morphology was determined to be more easily replicated if pores were stabilized by alumina additions and powders were dispersed evenly. In order to better characterize processing, high temperature and high ramp rate thermal decomposition data were gathered. It was found that the high ramp rate thermal decomposition behavior of several hydrides was more rapid than hydride kinetics at low ramp rates. This data was then used to estimate the contribution of several pore formation mechanisms to the development of pore structure. It was found that gas-metal eutectic growth can only be a viable pore formation mode if non-equilibrium conditions persist. Bubble capture cannot be a dominant pore growth mode due to high bubble terminal velocities. Direct gas evolution appears to be the most likely pore formation mode due to high gas evolution rate from the decomposing particulate and microstructural pore growth trends. The overall process was evaluated for its economic viability. It was found that thermal decomposition has potential for industrialization, but further refinements are necessary in order for the process to be viable.

Determination of knock characteristics in spark ignition engines: an approach based on ensemble empirical mode decomposition

NASA Astrophysics Data System (ADS)

Li, Ning; Yang, Jianguo; Zhou, Rui; Liang, Caiping

2016-04-01

Knock is one of the major constraints to improve the performance and thermal efficiency of spark ignition (SI) engines. It can also result in severe permanent engine damage under certain operating conditions. Based on the ensemble empirical mode decomposition (EEMD), this paper proposes a new approach to determine the knock characteristics in SI engines. By adding a uniformly distributed and finite white Gaussian noise, the EEMD can preserve signal continuity in different scales and therefore alleviates the mode-mixing problem occurring in the classic empirical mode decomposition (EMD). The feasibilities of applying the EEMD to detect the knock signatures of a test SI engine via the pressure signal measured from combustion chamber and the vibration signal measured from cylinder head are investigated. Experimental results show that the EEMD-based method is able to detect the knock signatures from both the pressure signal and vibration signal, even in initial stage of knock. Finally, by comparing the application results with those obtained by short-time Fourier transform (STFT), Wigner-Ville distribution (WVD) and discrete wavelet transform (DWT), the superiority of the EEMD method in determining knock characteristics is demonstrated.

A Compound Fault Diagnosis for Rolling Bearings Method Based on Blind Source Separation and Ensemble Empirical Mode Decomposition

PubMed Central

Wang, Huaqing; Li, Ruitong; Tang, Gang; Yuan, Hongfang; Zhao, Qingliang; Cao, Xi

2014-01-01

A Compound fault signal usually contains multiple characteristic signals and strong confusion noise, which makes it difficult to separate week fault signals from them through conventional ways, such as FFT-based envelope detection, wavelet transform or empirical mode decomposition individually. In order to improve the compound faults diagnose of rolling bearings via signals’ separation, the present paper proposes a new method to identify compound faults from measured mixed-signals, which is based on ensemble empirical mode decomposition (EEMD) method and independent component analysis (ICA) technique. With the approach, a vibration signal is firstly decomposed into intrinsic mode functions (IMF) by EEMD method to obtain multichannel signals. Then, according to a cross correlation criterion, the corresponding IMF is selected as the input matrix of ICA. Finally, the compound faults can be separated effectively by executing ICA method, which makes the fault features more easily extracted and more clearly identified. Experimental results validate the effectiveness of the proposed method in compound fault separating, which works not only for the outer race defect, but also for the rollers defect and the unbalance fault of the experimental system. PMID:25289644

Signal enhancement based on complex curvelet transform and complementary ensemble empirical mode decomposition

NASA Astrophysics Data System (ADS)

Dong, Lieqian; Wang, Deying; Zhang, Yimeng; Zhou, Datong

2017-09-01

Signal enhancement is a necessary step in seismic data processing. In this paper we utilize the complementary ensemble empirical mode decomposition (CEEMD) and complex curvelet transform (CCT) methods to separate signal from random noise further to improve the signal to noise (S/N) ratio. Firstly, the original data with noise is decomposed into a series of intrinsic mode function (IMF) profiles with the aid of CEEMD. Then the IMFs with noise are transformed into CCT domain. By choosing different thresholds which are based on the noise level difference of each IMF profile, the noise in original data can be suppressed. Finally, we illustrate the effectiveness of the approach by simulated and field datasets.

Modal characteristics of a simplified brake rotor model using semi-analytical Rayleigh Ritz method

NASA Astrophysics Data System (ADS)

Zhang, F.; Cheng, L.; Yam, L. H.; Zhou, L. M.

2006-10-01

Emphasis of this paper is given to the modal characteristics of a brake rotor which is utilized in automotive disc brake system. The brake rotor is modeled as a combined structure comprising an annular plate connected to a segment of cylindrical shell by distributed artificial springs. Modal analysis shows the existence of three types of modes for the combined structure, depending on the involvement of each substructure. A decomposition technique is proposed, allowing each mode of the combined structure to be decomposed into a linear combination of the individual substructure modes. It is shown that the decomposition coefficients provide a direct and systematic means to carry out modal classification and quantification.

Approaches to optimization of SS/TDMA time slot assignment. [satellite switched time division multiple access

NASA Technical Reports Server (NTRS)

Wade, T. O.

1984-01-01

Reduction techniques for traffic matrices are explored in some detail. These matrices arise in satellite switched time-division multiple access (SS/TDMA) techniques whereby switching of uplink and downlink beams is required to facilitate interconnectivity of beam zones. A traffic matrix is given to represent that traffic to be transmitted from n uplink beams to n downlink beams within a TDMA frame typically of 1 ms duration. The frame is divided into segments of time and during each segment a portion of the traffic is represented by a switching mode. This time slot assignment is characterized by a mode matrix in which there is not more than a single non-zero entry on each line (row or column) of the matrix. Investigation is confined to decomposition of an n x n traffic matrix by mode matrices with a requirement that the decomposition be 100 percent efficient or, equivalently, that the line(s) in the original traffic matrix whose sum is maximal (called critical line(s)) remain maximal as mode matrices are subtracted throughout the decomposition process. A method of decomposition of an n x n traffic matrix by mode matrices results in a number of steps that is bounded by n(2) - 2n + 2. It is shown that this upper bound exists for an n x n matrix wherein all the lines are maximal (called a quasi doubly stochastic (QDS) matrix) or for an n x n matrix that is completely arbitrary. That is, the fact that no method can exist with a lower upper bound is shown for both QDS and arbitrary matrices, in an elementary and straightforward manner.

Domain decomposition for aerodynamic and aeroacoustic analyses, and optimization

NASA Technical Reports Server (NTRS)

Baysal, Oktay

1995-01-01

The overarching theme was the domain decomposition, which intended to improve the numerical solution technique for the partial differential equations at hand; in the present study, those that governed either the fluid flow, or the aeroacoustic wave propagation, or the sensitivity analysis for a gradient-based optimization. The role of the domain decomposition extended beyond the original impetus of discretizing geometrical complex regions or writing modular software for distributed-hardware computers. It induced function-space decompositions and operator decompositions that offered the valuable property of near independence of operator evaluation tasks. The objectives have gravitated about the extensions and implementations of either the previously developed or concurrently being developed methodologies: (1) aerodynamic sensitivity analysis with domain decomposition (SADD); (2) computational aeroacoustics of cavities; and (3) dynamic, multibody computational fluid dynamics using unstructured meshes.

Density-dependent liquid nitromethane decomposition: molecular dynamics simulations based on ReaxFF.

PubMed

Rom, Naomi; Zybin, Sergey V; van Duin, Adri C T; Goddard, William A; Zeiri, Yehuda; Katz, Gil; Kosloff, Ronnie

2011-09-15

The decomposition mechanism of hot liquid nitromethane at various compressions was studied using reactive force field (ReaxFF) molecular dynamics simulations. A competition between two different initial thermal decomposition schemes is observed, depending on compression. At low densities, unimolecular C-N bond cleavage is the dominant route, producing CH(3) and NO(2) fragments. As density and pressure rise approaching the Chapman-Jouget detonation conditions (∼30% compression, >2500 K) the dominant mechanism switches to the formation of the CH(3)NO fragment via H-transfer and/or N-O bond rupture. The change in the decomposition mechanism of hot liquid NM leads to a different kinetic and energetic behavior, as well as products distribution. The calculated density dependence of the enthalpy change correlates with the change in initial decomposition reaction mechanism. It can be used as a convenient and useful global parameter for the detection of reaction dynamics. Atomic averaged local diffusion coefficients are shown to be sensitive to the reactions dynamics, and can be used to distinguish between time periods where chemical reactions occur and diffusion-dominated, nonreactive time periods. © 2011 American Chemical Society

Empirical mode decomposition apparatus, method and article of manufacture for analyzing biological signals and performing curve fitting

NASA Technical Reports Server (NTRS)

Huang, Norden E. (Inventor)

2004-01-01

A computer implemented physical signal analysis method includes four basic steps and the associated presentation techniques of the results. The first step is a computer implemented Empirical Mode Decomposition that extracts a collection of Intrinsic Mode Functions (IMF) from nonlinear, nonstationary physical signals. The decomposition is based on the direct extraction of the energy associated with various intrinsic time scales in the physical signal. Expressed in the IMF's, they have well-behaved Hilbert Transforms from which instantaneous frequencies can be calculated. The second step is the Hilbert Transform which produces a Hilbert Spectrum. Thus, the invention can localize any event on the time as well as the frequency axis. The decomposition can also be viewed as an expansion of the data in terms of the IMF's. Then, these IMF's, based on and derived from the data, can serve as the basis of that expansion. The local energy and the instantaneous frequency derived from the IMF's through the Hilbert transform give a full energy-frequency-time distribution of the data which is designated as the Hilbert Spectrum. The third step filters the physical signal by combining a subset of the IMFs. In the fourth step, a curve may be fitted to the filtered signal which may not have been possible with the original, unfiltered signal.

Empirical mode decomposition apparatus, method and article of manufacture for analyzing biological signals and performing curve fitting

NASA Technical Reports Server (NTRS)

Huang, Norden E. (Inventor)

2002-01-01

A computer implemented physical signal analysis method includes four basic steps and the associated presentation techniques of the results. The first step is a computer implemented Empirical Mode Decomposition that extracts a collection of Intrinsic Mode Functions (IMF) from nonlinear, nonstationary physical signals. The decomposition is based on the direct extraction of the energy associated with various intrinsic time scales in the physical signal. Expressed in the IMF's, they have well-behaved Hilbert Transforms from which instantaneous frequencies can be calculated. The second step is the Hilbert Transform which produces a Hilbert Spectrum. Thus, the invention can localize any event on the time as well as the frequency axis. The decomposition can also be viewed as an expansion of the data in terms of the IMF's. Then, these IMF's, based on and derived from the data, can serve as the basis of that expansion. The local energy and the instantaneous frequency derived from the IMF's through the Hilbert transform give a full energy-frequency-time distribution of the data which is designated as the Hilbert Spectrum. The third step filters the physical signal by combining a subset of the IMFs. In the fourth step, a curve may be fitted to the filtered signal which may not have been possible with the original, unfiltered signal.

Computer implemented empirical mode decomposition method apparatus, and article of manufacture utilizing curvature extrema

NASA Technical Reports Server (NTRS)

Shen, Zheng (Inventor); Huang, Norden Eh (Inventor)

2003-01-01

A computer implemented physical signal analysis method is includes two essential steps and the associated presentation techniques of the results. All the steps exist only in a computer: there are no analytic expressions resulting from the method. The first step is a computer implemented Empirical Mode Decomposition to extract a collection of Intrinsic Mode Functions (IMF) from nonlinear, nonstationary physical signals based on local extrema and curvature extrema. The decomposition is based on the direct extraction of the energy associated with various intrinsic time scales in the physical signal. Expressed in the IMF's, they have well-behaved Hilbert Transforms from which instantaneous frequencies can be calculated. The second step is the Hilbert Transform. The final result is the Hilbert Spectrum. Thus, the invention can localize any event on the time as well as the frequency axis. The decomposition can also be viewed as an expansion of the data in terms of the IMF's. Then, these IMF's, based on and derived from the data, can serve as the basis of that expansion. The local energy and the instantaneous frequency derived from the IMF's through the Hilbert transform give a full energy-frequency-time distribution of the data which is designated as the Hilbert Spectrum.

Research and application of a novel hybrid decomposition-ensemble learning paradigm with error correction for daily PM10 forecasting

NASA Astrophysics Data System (ADS)

Luo, Hongyuan; Wang, Deyun; Yue, Chenqiang; Liu, Yanling; Guo, Haixiang

2018-03-01

In this paper, a hybrid decomposition-ensemble learning paradigm combining error correction is proposed for improving the forecast accuracy of daily PM10 concentration. The proposed learning paradigm is consisted of the following two sub-models: (1) PM10 concentration forecasting model; (2) error correction model. In the proposed model, fast ensemble empirical mode decomposition (FEEMD) and variational mode decomposition (VMD) are applied to disassemble original PM10 concentration series and error sequence, respectively. The extreme learning machine (ELM) model optimized by cuckoo search (CS) algorithm is utilized to forecast the components generated by FEEMD and VMD. In order to prove the effectiveness and accuracy of the proposed model, two real-world PM10 concentration series respectively collected from Beijing and Harbin located in China are adopted to conduct the empirical study. The results show that the proposed model performs remarkably better than all other considered models without error correction, which indicates the superior performance of the proposed model.

Parameterizing Coefficients of a POD-Based Dynamical System

NASA Technical Reports Server (NTRS)

Kalb, Virginia L.

2010-01-01

A method of parameterizing the coefficients of a dynamical system based of a proper orthogonal decomposition (POD) representing the flow dynamics of a viscous fluid has been introduced. (A brief description of POD is presented in the immediately preceding article.) The present parameterization method is intended to enable construction of the dynamical system to accurately represent the temporal evolution of the flow dynamics over a range of Reynolds numbers. The need for this or a similar method arises as follows: A procedure that includes direct numerical simulation followed by POD, followed by Galerkin projection to a dynamical system has been proven to enable representation of flow dynamics by a low-dimensional model at the Reynolds number of the simulation. However, a more difficult task is to obtain models that are valid over a range of Reynolds numbers. Extrapolation of low-dimensional models by use of straightforward Reynolds-number-based parameter continuation has proven to be inadequate for successful prediction of flows. A key part of the problem of constructing a dynamical system to accurately represent the temporal evolution of the flow dynamics over a range of Reynolds numbers is the problem of understanding and providing for the variation of the coefficients of the dynamical system with the Reynolds number. Prior methods do not enable capture of temporal dynamics over ranges of Reynolds numbers in low-dimensional models, and are not even satisfactory when large numbers of modes are used. The basic idea of the present method is to solve the problem through a suitable parameterization of the coefficients of the dynamical system. The parameterization computations involve utilization of the transfer of kinetic energy between modes as a function of Reynolds number. The thus-parameterized dynamical system accurately predicts the flow dynamics and is applicable to a range of flow problems in the dynamical regime around the Hopf bifurcation. Parameter-continuation software can be used on the parameterized dynamical system to derive a bifurcation diagram that accurately predicts the temporal flow behavior.

Vibrational Population Distribution in Formaldehyde Expanding from Chen Pyrolysis Nozzle Measured by Chirped Pulse Millimeter Wave Spectroscopy

NASA Astrophysics Data System (ADS)

Kuyanov-Prozument, Kirill; Vasiliou, Angayle; Park, G. Barratt; Muenter, John S.; Stanton, John F.; Ellison, G. Barney; Field, Robert W.

2011-06-01

Knowing the vibrational population distribution of unimolecular fragmentation reaction products can reveal the reaction mechanism. Here, we applied Chirped Pulse Millimeter Wave (CPmmW) spectroscopy, invented by Brooks Pate and co-workers, to detect the vibrational population distribution of formaldehyde produced by pyrolysis of methyl nitrite (CH_3ONO) or ethyl nitrite (CH_3CH_2ONO). The pure rotational spectrum contains information about vibrational populations via the known vibration dependence of the rotational constants, which is easily observed in the millimeter-wave spectrum. Only two of six vibrational modes of formaldehyde are significantly populated in both pyrolysis decomposition reactions and in an expansion of pure formaldehyde, suggesting that it is the collisional energy transfer that primarily determines the vibrational population distribution. The non-Boltzmann population distribution among the observed vibrational modes demonstrates non-statistical vibrational energy transfer in formaldehyde. It is in sharp contrast with the equilibrated population distribution measured in OCS and the almost complete vibrational relaxation observed in acetaldehyde. This work is supported by grants from the US Department of Energy and the ACS Petroleum Research Fund, and the National Science Foundation grant "Organic Radicals in Biomass Decomposition: Mechanisms & Dynamics," (CHE-0848606) G. G. Brown, B. C. Dian, K. O. Douglass, S. M. Geyer, S. T. Shipman and B. H. Pate Rev. Sci. Instrum. 79, 053103 (1995).

Data analysis using a combination of independent component analysis and empirical mode decomposition

NASA Astrophysics Data System (ADS)

Lin, Shih-Lin; Tung, Pi-Cheng; Huang, Norden E.

2009-06-01

A combination of independent component analysis and empirical mode decomposition (ICA-EMD) is proposed in this paper to analyze low signal-to-noise ratio data. The advantages of ICA-EMD combination are these: ICA needs few sensory clues to separate the original source from unwanted noise and EMD can effectively separate the data into its constituting parts. The case studies reported here involve original sources contaminated by white Gaussian noise. The simulation results show that the ICA-EMD combination is an effective data analysis tool.

Fundamental Studies of Beta Phase Decomposition Modes in Titanium Alloys

DTIC Science & Technology

1989-01-31

and H. I. Aaronson, "The Carbon-Carbon Interaction Energy in Alpha Fe- C Alloys", Acta Met., in press. Raju V. Ramanujan , H. I. Aaronson and P. H. Leo...ACCESSIO% %. C 20332 61102F 2306 Al 11 TITLE (Include Security Classification) FUNDAMENTAL STUDIES OF BETA PHASE DECOMPOSITION MODES IN TITANIUM ALLOYS 12...SECUR1Tv CLASSiI-CAtION M) UNCLASSIFIED/UNLIMITED C SAME AS RPT C ] YfC ’SERS UNCLASSIFIED 22a NAME OF RESPONSIBLE INOI’JIDUAL 22b TELEPwONE (Include Area

Application of empirical mode decomposition with local linear quantile regression in financial time series forecasting.

PubMed

Jaber, Abobaker M; Ismail, Mohd Tahir; Altaher, Alsaidi M

2014-01-01

This paper mainly forecasts the daily closing price of stock markets. We propose a two-stage technique that combines the empirical mode decomposition (EMD) with nonparametric methods of local linear quantile (LLQ). We use the proposed technique, EMD-LLQ, to forecast two stock index time series. Detailed experiments are implemented for the proposed method, in which EMD-LPQ, EMD, and Holt-Winter methods are compared. The proposed EMD-LPQ model is determined to be superior to the EMD and Holt-Winter methods in predicting the stock closing prices.

Analysis and Prediction of Sea Ice Evolution using Koopman Mode Decomposition Techniques

DTIC Science & Technology

Koopman Mode Analysis was newly applied to southern hemisphere sea ice concentration data. The resulting Koopman modes from analysis of both the...southern and northern hemisphere sea ice concentration data shows geographical regions where sea ice coverage has decreased over multiyear time scales.

Nano- and micro-electromechanical switch dynamics

NASA Astrophysics Data System (ADS)

Pulskamp, Jeffrey S.; Proie, Robert M.; Polcawich, Ronald G.

2013-01-01

This paper reports theoretical analysis and experimental results on the dynamics of piezoelectric MEMS mechanical logic relays. The multiple degree of freedom analytical model, based on modal decomposition, utilizes modal parameters obtained from finite element analysis and an analytical model of piezoelectric actuation. The model accounts for exact device geometry, damping, drive waveform variables, and high electric field piezoelectric nonlinearity. The piezoelectrically excited modal force is calculated directly and provides insight into design optimization for switching speed. The model accurately predicts the propagation delay dependence on actuation voltage of mechanically distinct relay designs. The model explains the observed discrepancies in switching speed of these devices relative to single degree of freedom switching speed models and suggests the strong potential for improved switching speed performance in relays designed for mechanical logic and RF circuits through the exploitation of higher order vibrational modes.

Integrated ensemble noise-reconstructed empirical mode decomposition for mechanical fault detection

NASA Astrophysics Data System (ADS)

Yuan, Jing; Ji, Feng; Gao, Yuan; Zhu, Jun; Wei, Chenjun; Zhou, Yu

2018-05-01

A new branch of fault detection is utilizing the noise such as enhancing, adding or estimating the noise so as to improve the signal-to-noise ratio (SNR) and extract the fault signatures. Hereinto, ensemble noise-reconstructed empirical mode decomposition (ENEMD) is a novel noise utilization method to ameliorate the mode mixing and denoised the intrinsic mode functions (IMFs). Despite the possibility of superior performance in detecting weak and multiple faults, the method still suffers from the major problems of the user-defined parameter and the powerless capability for a high SNR case. Hence, integrated ensemble noise-reconstructed empirical mode decomposition is proposed to overcome the drawbacks, improved by two noise estimation techniques for different SNRs as well as the noise estimation strategy. Independent from the artificial setup, the noise estimation by the minimax thresholding is improved for a low SNR case, which especially shows an outstanding interpretation for signature enhancement. For approximating the weak noise precisely, the noise estimation by the local reconfiguration using singular value decomposition (SVD) is proposed for a high SNR case, which is particularly powerful for reducing the mode mixing. Thereinto, the sliding window for projecting the phase space is optimally designed by the correlation minimization. Meanwhile, the reasonable singular order for the local reconfiguration to estimate the noise is determined by the inflection point of the increment trend of normalized singular entropy. Furthermore, the noise estimation strategy, i.e. the selection approaches of the two estimation techniques along with the critical case, is developed and discussed for different SNRs by means of the possible noise-only IMF family. The method is validated by the repeatable simulations to demonstrate the synthetical performance and especially confirm the capability of noise estimation. Finally, the method is applied to detect the local wear fault from a dual-axis stabilized platform and the gear crack from an operating electric locomotive to verify its effectiveness and feasibility.

Detecting brain dynamics during resting state: a tensor based evolutionary clustering approach

NASA Astrophysics Data System (ADS)

Al-sharoa, Esraa; Al-khassaweneh, Mahmood; Aviyente, Selin

2017-08-01

Human brain is a complex network with connections across different regions. Understanding the functional connectivity (FC) of the brain is important both during resting state and task; as disruptions in connectivity patterns are indicators of different psychopathological and neurological diseases. In this work, we study the resting state functional connectivity networks (FCNs) of the brain from fMRI BOLD signals. Recent studies have shown that FCNs are dynamic even during resting state and understanding the temporal dynamics of FCNs is important for differentiating between different conditions. Therefore, it is important to develop algorithms to track the dynamic formation and dissociation of FCNs of the brain during resting state. In this paper, we propose a two step tensor based community detection algorithm to identify and track the brain network community structure across time. First, we introduce an information-theoretic function to reduce the dynamic FCN and identify the time points that are similar topologically to combine them into a tensor. These time points will be used to identify the different FC states. Second, a tensor based spectral clustering approach is developed to identify the community structure of the constructed tensors. The proposed algorithm applies Tucker decomposition to the constructed tensors and extract the orthogonal factor matrices along the connectivity mode to determine the common subspace within each FC state. The detected community structure is summarized and described as FC states. The results illustrate the dynamic structure of resting state networks (RSNs), including the default mode network, somatomotor network, subcortical network and visual network.

Dynamics in the Decompositions Approach to Quantum Mechanics

NASA Astrophysics Data System (ADS)

Harding, John

2017-12-01

In Harding (Trans. Amer. Math. Soc. 348(5), 1839-1862 1996) it was shown that the direct product decompositions of any non-empty set, group, vector space, and topological space X form an orthomodular poset Fact X. This is the basis for a line of study in foundational quantum mechanics replacing Hilbert spaces with other types of structures. Here we develop dynamics and an abstract version of a time independent Schrödinger's equation in the setting of decompositions by considering representations of the group of real numbers in the automorphism group of the orthomodular poset Fact X of decompositions.

Interannual coherent variability of SSTA and SSHA in the Tropical Indian Ocean

NASA Astrophysics Data System (ADS)

Feng, J. Q.

2012-01-01

Sea surface height derived from the multiple ocean satellite altimeter missions (TOPEX/Poseidon, Jason-1, ERS, Envisat et al.) and sea surface temperature from National Centers for Environmental Prediction (NCEP) over 1993-2008 are analyzed to investigate the coherent patterns between the interannual variability of the sea surface and subsurface in the Tropical Indian Ocean, by jointly adopting Singular Value Decomposition (SVD) and Extended Associate Pattern Analysis (EAPA) methods. Results show that there are two dominant coherent modes with the nearly same main period of about 3-5 yr, accounting for 86 % of the total covariance in all, but 90° phase difference between them. The primary pattern is characterized by a east-west dipole mode associated with the mature phase of ENSO, and the second presents a sandwich mode having one sign anomalies along Sumatra-Java coast and northeast of Madagascar, whilst an opposite sign between the two regions. The robust correlations of the sea surface height anomaly (SSHA) with sea surface temperature anomaly (SSTA) in the leading modes indicate a strong interaction between them, though the highest correlation coefficient appears with a time lag. And there may be some physical significance with respect to ocean dynamics implied in SSHA variability. Analyzing results show that the features of oceanic waves with basin scale, of which the Rossby wave is prominent, are apparent in the dominant modes. It is further demonstrated from the EAPA that the equatorial eastward Kelvin wave and off-equatorial westward Rossby wave as well as their reflection in the east and west boundary, respectively, are important dynamic mechanisms in the evolution of the two leading coherent patterns. Results of the present study suggest that the upper ocean thermal variations on the timescale of interannual coherent with the ocean dynamics in spatial structure and temporal evolution are mainly attributed to the ocean waves.

Organic Carbon Sorption and Decomposition in Selected Global Soils

DOE Data Explorer

Jagadamma, S.; Mayes, M. A.; Steinweg, J. M.; Wang, G.; Post, W. M.

2014-01-01

This data set reports the results of lab-scale experiments conducted to investigate the dynamics of organic carbon (C) decomposition from several soils from temperate, tropical, arctic, and sub-arctic environments. Results were used to test the newly developed soil microbe decomposition C model--Microbial-ENzyme-medicated Decomposition (MEND).

Application of singular value decomposition to structural dynamics systems with constraints

NASA Technical Reports Server (NTRS)

Juang, J.-N.; Pinson, L. D.

1985-01-01

Singular value decomposition is used to construct a coordinate transformation for a linear dynamic system subject to linear, homogeneous constraint equations. The method is compared with two commonly used methods, namely classical Gaussian elimination and Walton-Steeves approach. Although the classical method requires fewer numerical operations, the singular value decomposition method is more accurate and convenient in eliminating the dependent coordinates. Numerical examples are presented to demonstrate the application of the method.

Empirical mode decomposition-based facial pose estimation inside video sequences

NASA Astrophysics Data System (ADS)

Qing, Chunmei; Jiang, Jianmin; Yang, Zhijing

2010-03-01

We describe a new pose-estimation algorithm via integration of the strength in both empirical mode decomposition (EMD) and mutual information. While mutual information is exploited to measure the similarity between facial images to estimate poses, EMD is exploited to decompose input facial images into a number of intrinsic mode function (IMF) components, which redistribute the effect of noise, expression changes, and illumination variations as such that, when the input facial image is described by the selected IMF components, all the negative effects can be minimized. Extensive experiments were carried out in comparisons to existing representative techniques, and the results show that the proposed algorithm achieves better pose-estimation performances with robustness to noise corruption, illumination variation, and facial expressions.

X-Ray Thomson Scattering Without the Chihara Decomposition

NASA Astrophysics Data System (ADS)

Magyar, Rudolph; Baczewski, Andrew; Shulenburger, Luke; Hansen, Stephanie B.; Desjarlais, Michael P.; Sandia National Laboratories Collaboration

X-Ray Thomson Scattering is an important experimental technique used in dynamic compression experiments to measure the properties of warm dense matter. The fundamental property probed in these experiments is the electronic dynamic structure factor that is typically modeled using an empirical three-term decomposition (Chihara, J. Phys. F, 1987). One of the crucial assumptions of this decomposition is that the system's electrons can be either classified as bound to ions or free. This decomposition may not be accurate for materials in the warm dense regime. We present unambiguous first principles calculations of the dynamic structure factor independent of the Chihara decomposition that can be used to benchmark these assumptions. Results are generated using a finite-temperature real-time time-dependent density functional theory applied for the first time in these conditions. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Security Administration under contract DE-AC04-94AL85000.

Dynamics of multiple elements in fast decomposing vegetable residues.

PubMed

Cao, Chun; Liu, Si-Qi; Ma, Zhen-Bang; Lin, Yun; Su, Qiong; Chen, Huan; Wang, Jun-Jian

2018-03-01

Litter decomposition regulates the cycling of nutrients and toxicants but is poorly studied in farmlands. To understand the unavoidable in-situ decomposition process, we quantified the dynamics of C, H, N, As, Ca, Cd, Cr, Cu, Fe, Hg, K, Mg, Mn, Na, Ni, Pb, and Zn during a 180-d decomposition study in leafy lettuce (Lactuca sativa var. longifoliaf) and rape (Brassica chinensis) residues in a wastewater-irrigated farmland in northwestern China. Different from most studied natural ecosystems, the managed vegetable farmland had a much faster litter decomposition rate (half-life of 18-60d), and interestingly, faster decomposition of roots relative to leaves for both the vegetables. Faster root decomposition can be explained by the initial biochemical composition (more O-alkyl C and less alkyl and aromatic C) but not the C/N stoichiometry. Multi-element dynamics varied greatly, with C, H, N, K, and Na being highly released (remaining proportion<20%), Ca, Cd, Cr, Mg, Ni, and Zn released, and As, Cu, Fe, Hg, Mn, and Pb possibly accumulated. Although vegetable residues serve as temporary sinks of some metal(loid)s, their fast decomposition, particularly for the O-alkyl-C-rich leafy-lettuce roots, suggest that toxic metal(loid)s can be released from residues, which therefore become secondary pollution sources. Copyright © 2017 Elsevier B.V. All rights reserved.

Water-separated ion pairs cause the slow dielectric mode of magnesium sulfate solutions

NASA Astrophysics Data System (ADS)

Mamatkulov, Shavkat I.; Rinne, Klaus F.; Buchner, Richard; Netz, Roland R.; Bonthuis, Douwe Jan

2018-06-01

We compare the dielectric spectra of aqueous MgSO4 and Na2SO4 solutions calculated from classical molecular dynamics simulations with experimental data, using an optimized thermodynamically consistent sulfate force field. Both the concentration-dependent shift of the static dielectric constant and the spectral shape match the experimental results very well for Na2SO4 solutions. For MgSO4 solutions, the simulations qualitatively reproduce the experimental observation of a slow mode, the origin of which we trace back to the ion-pair relaxation contribution via spectral decomposition. The radial distribution functions show that Mg2+ and SO42 - ions form extensive water-separated—and thus strongly dipolar—ion pairs, the orientational relaxation of which provides a simple physical explanation for the prominent slow dielectric mode in MgSO4 solutions. Remarkably, the Mg2+-SO42 - ion-pair relaxation extends all the way into the THz range, which we rationalize by the vibrational relaxation of tightly bound water-separated ion pairs. Thus, the relaxation of divalent ion pairs can give rise to widely separated orientational and vibrational spectroscopic features.

GPR random noise reduction using BPD and EMD

NASA Astrophysics Data System (ADS)

Ostoori, Roya; Goudarzi, Alireza; Oskooi, Behrooz

2018-04-01

Ground-penetrating radar (GPR) exploration is a new high-frequency technology that explores near-surface objects and structures accurately. The high-frequency antenna of the GPR system makes it a high-resolution method compared to other geophysical methods. The frequency range of recorded GPR is so wide that random noise recording is inevitable due to acquisition. This kind of noise comes from unknown sources and its correlation to the adjacent traces is nearly zero. This characteristic of random noise along with the higher accuracy of GPR system makes denoising very important for interpretable results. The main objective of this paper is to reduce GPR random noise based on pursuing denoising using empirical mode decomposition. Our results showed that empirical mode decomposition in combination with basis pursuit denoising (BPD) provides satisfactory outputs due to the sifting process compared to the time-domain implementation of the BPD method on both synthetic and real examples. Our results demonstrate that because of the high computational costs, the BPD-empirical mode decomposition technique should only be used for heavily noisy signals.

Effects of lithium insertion on thermal conductivity of silicon nanowires

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xu, Wen; Institute of High Performance Computing, A*STAR, Singapore, Singapore 138632; Zhang, Gang, E-mail: zhangg@ihpc.a-star.edu.sg

2015-04-27

Recently, silicon nanowires (SiNWs) have been applied as high-performance Li battery anodes, since they can overcome the pulverization and mechanical fracture during lithiation. Although thermal stability is one of the most important parameters that determine safety of Li batteries, thermal conductivity of SiNWs with Li insertion remains unclear. In this letter, using molecular dynamics simulations, we study room temperature thermal conductivity of SiNWs with Li insertion. It is found that compared with the pristine SiNW, there is as much as 60% reduction in thermal conductivity with 10% concentration of inserted Li atoms, while under the same impurity concentration the reductionmore » in thermal conductivity of the mass-disordered SiNW is only 30%. With lattice dynamics calculations and normal mode decomposition, it is revealed that the phonon lifetimes in SiNWs decrease greatly due to strong scattering of phonons by vibrational modes of Li atoms, especially for those high frequency phonons. The observed strong phonon scattering phenomenon in Li-inserted SiNWs is similar to the phonon rattling effect. Our study serves as an exploration of thermal properties of SiNWs as Li battery anodes or weakly coupled with impurity atoms.« less

Effects of lithium insertion on thermal conductivity of silicon nanowires

NASA Astrophysics Data System (ADS)

Xu, Wen; Zhang, Gang; Li, Baowen

2015-04-01

Recently, silicon nanowires (SiNWs) have been applied as high-performance Li battery anodes, since they can overcome the pulverization and mechanical fracture during lithiation. Although thermal stability is one of the most important parameters that determine safety of Li batteries, thermal conductivity of SiNWs with Li insertion remains unclear. In this letter, using molecular dynamics simulations, we study room temperature thermal conductivity of SiNWs with Li insertion. It is found that compared with the pristine SiNW, there is as much as 60% reduction in thermal conductivity with 10% concentration of inserted Li atoms, while under the same impurity concentration the reduction in thermal conductivity of the mass-disordered SiNW is only 30%. With lattice dynamics calculations and normal mode decomposition, it is revealed that the phonon lifetimes in SiNWs decrease greatly due to strong scattering of phonons by vibrational modes of Li atoms, especially for those high frequency phonons. The observed strong phonon scattering phenomenon in Li-inserted SiNWs is similar to the phonon rattling effect. Our study serves as an exploration of thermal properties of SiNWs as Li battery anodes or weakly coupled with impurity atoms.

Hilbert-Huang transform analysis of dynamic and earthquake motion recordings

USGS Publications Warehouse

Zhang, R.R.; Ma, S.; Safak, E.; Hartzell, S.

2003-01-01

This study examines the rationale of Hilbert-Huang transform (HHT) for analyzing dynamic and earthquake motion recordings in studies of seismology and engineering. In particular, this paper first provides the fundamentals of the HHT method, which consist of the empirical mode decomposition (EMD) and the Hilbert spectral analysis. It then uses the HHT to analyze recordings of hypothetical and real wave motion, the results of which are compared with the results obtained by the Fourier data processing technique. The analysis of the two recordings indicates that the HHT method is able to extract some motion characteristics useful in studies of seismology and engineering, which might not be exposed effectively and efficiently by Fourier data processing technique. Specifically, the study indicates that the decomposed components in EMD of HHT, namely, the intrinsic mode function (IMF) components, contain observable, physical information inherent to the original data. It also shows that the grouped IMF components, namely, the EMD-based low- and high-frequency components, can faithfully capture low-frequency pulse-like as well as high-frequency wave signals. Finally, the study illustrates that the HHT-based Hilbert spectra are able to reveal the temporal-frequency energy distribution for motion recordings precisely and clearly.

Fluid dynamic propagation of initial baryon number perturbations on a Bjorken flow background

DOE PAGES

Floerchinger, Stefan; Martinez, Mauricio

2015-12-11

Baryon number density perturbations offer a possible route to experimentally measure baryon number susceptibilities and heat conductivity of the quark gluon plasma. We study the fluid dynamical evolution of local and event-by-event fluctuations of baryon number density, flow velocity, and energy density on top of a (generalized) Bjorken expansion. To that end we use a background-fluctuation splitting and a Bessel-Fourier decomposition for the fluctuating part of the fluid dynamical fields with respect to the azimuthal angle, the radius in the transverse plane, and rapidity. Here, we examine how the time evolution of linear perturbations depends on the equation of statemore » as well as on shear viscosity, bulk viscosity, and heat conductivity for modes with different azimuthal, radial, and rapidity wave numbers. Finally we discuss how this information is accessible to experiments in terms of the transverse and rapidity dependence of correlation functions for baryonic particles in high energy nuclear collisions.« less

Higher-order stochastic differential equations and the positive Wigner function

NASA Astrophysics Data System (ADS)

Drummond, P. D.

2017-12-01

General higher-order stochastic processes that correspond to any diffusion-type tensor of higher than second order are obtained. The relationship of multivariate higher-order stochastic differential equations with tensor decomposition theory and tensor rank is explained. Techniques for generating the requisite complex higher-order noise are proved to exist either using polar coordinates and γ distributions, or from products of Gaussian variates. This method is shown to allow the calculation of the dynamics of the Wigner function, after it is extended to a complex phase space. The results are illustrated physically through dynamical calculations of the positive Wigner distribution for three-mode parametric downconversion, widely used in quantum optics. The approach eliminates paradoxes arising from truncation of the higher derivative terms in Wigner function time evolution. Anomalous results of negative populations and vacuum scattering found in truncated Wigner quantum simulations in quantum optics and Bose-Einstein condensate dynamics are shown not to occur with this type of stochastic theory.

Vision-based system identification technique for building structures using a motion capture system

NASA Astrophysics Data System (ADS)

Oh, Byung Kwan; Hwang, Jin Woo; Kim, Yousok; Cho, Tongjun; Park, Hyo Seon

2015-11-01

This paper presents a new vision-based system identification (SI) technique for building structures by using a motion capture system (MCS). The MCS with outstanding capabilities for dynamic response measurements can provide gage-free measurements of vibrations through the convenient installation of multiple markers. In this technique, from the dynamic displacement responses measured by MCS, the dynamic characteristics (natural frequency, mode shape, and damping ratio) of building structures are extracted after the processes of converting the displacement from MCS to acceleration and conducting SI by frequency domain decomposition. A free vibration experiment on a three-story shear frame was conducted to validate the proposed technique. The SI results from the conventional accelerometer-based method were compared with those from the proposed technique and showed good agreement, which confirms the validity and applicability of the proposed vision-based SI technique for building structures. Furthermore, SI directly employing MCS measured displacements to FDD was performed and showed identical results to those of conventional SI method.

Three-pattern decomposition of global atmospheric circulation: part I—decomposition model and theorems

NASA Astrophysics Data System (ADS)

Hu, Shujuan; Chou, Jifan; Cheng, Jianbo

2018-04-01

In order to study the interactions between the atmospheric circulations at the middle-high and low latitudes from the global perspective, the authors proposed the mathematical definition of three-pattern circulations, i.e., horizontal, meridional and zonal circulations with which the actual atmospheric circulation is expanded. This novel decomposition method is proved to accurately describe the actual atmospheric circulation dynamics. The authors used the NCEP/NCAR reanalysis data to calculate the climate characteristics of those three-pattern circulations, and found that the decomposition model agreed with the observed results. Further dynamical analysis indicates that the decomposition model is more accurate to capture the major features of global three dimensional atmospheric motions, compared to the traditional definitions of Rossby wave, Hadley circulation and Walker circulation. The decomposition model for the first time realized the decomposition of global atmospheric circulation using three orthogonal circulations within the horizontal, meridional and zonal planes, offering new opportunities to study the large-scale interactions between the middle-high latitudes and low latitudes circulations.

A hybrid filtering method based on a novel empirical mode decomposition for friction signals

NASA Astrophysics Data System (ADS)

Li, Chengwei; Zhan, Liwei

2015-12-01

During a measurement, the measured signal usually contains noise. To remove the noise and preserve the important feature of the signal, we introduce a hybrid filtering method that uses a new intrinsic mode function (NIMF) and a modified Hausdorff distance. The NIMF is defined as the difference between the noisy signal and each intrinsic mode function (IMF), which is obtained by empirical mode decomposition (EMD), ensemble EMD, complementary ensemble EMD, or complete ensemble EMD with adaptive noise (CEEMDAN). The relevant mode selecting is based on the similarity between the first NIMF and the rest of the NIMFs. With this filtering method, the EMD and improved versions are used to filter the simulation and friction signals. The friction signal between an airplane tire and the runaway is recorded during a simulated airplane touchdown and features spikes of various amplitudes and noise. The filtering effectiveness of the four hybrid filtering methods are compared and discussed. The results show that the filtering method based on CEEMDAN outperforms other signal filtering methods.

Long memory in international financial markets trends and short movements during 2008 financial crisis based on variational mode decomposition and detrended fluctuation analysis

NASA Astrophysics Data System (ADS)

Lahmiri, Salim

2015-11-01

The purpose of this study is to investigate long-range dependence in trend and short variation of stock market price and return series before, during, and after 2008 financial crisis. Variational mode decomposition (VMD), a newly introduced technique for signal processing, is adopted to decompose stock market data into a finite set of modes so as to obtain long term trends and short term movements of stock market data. Then, the detrended fluctuation analysis (DFA) and range scale (R/S) analysis are used to estimate Hurst exponent in each variational mode obtained from VMD. For both price and return series, the empirical results from twelve international stock markets show evidence that long term trends are persistent, whilst short term variations are anti-persistent before, during, and after 2008 financial crisis.

Planetary Gears Feature Extraction and Fault Diagnosis Method Based on VMD and CNN.

PubMed

Liu, Chang; Cheng, Gang; Chen, Xihui; Pang, Yusong

2018-05-11

Given local weak feature information, a novel feature extraction and fault diagnosis method for planetary gears based on variational mode decomposition (VMD), singular value decomposition (SVD), and convolutional neural network (CNN) is proposed. VMD was used to decompose the original vibration signal to mode components. The mode matrix was partitioned into a number of submatrices and local feature information contained in each submatrix was extracted as a singular value vector using SVD. The singular value vector matrix corresponding to the current fault state was constructed according to the location of each submatrix. Finally, by training a CNN using singular value vector matrices as inputs, planetary gear fault state identification and classification was achieved. The experimental results confirm that the proposed method can successfully extract local weak feature information and accurately identify different faults. The singular value vector matrices of different fault states have a distinct difference in element size and waveform. The VMD-based partition extraction method is better than ensemble empirical mode decomposition (EEMD), resulting in a higher CNN total recognition rate of 100% with fewer training times (14 times). Further analysis demonstrated that the method can also be applied to the degradation recognition of planetary gears. Thus, the proposed method is an effective feature extraction and fault diagnosis technique for planetary gears.

Planetary Gears Feature Extraction and Fault Diagnosis Method Based on VMD and CNN

PubMed Central

Cheng, Gang; Chen, Xihui

2018-01-01

Given local weak feature information, a novel feature extraction and fault diagnosis method for planetary gears based on variational mode decomposition (VMD), singular value decomposition (SVD), and convolutional neural network (CNN) is proposed. VMD was used to decompose the original vibration signal to mode components. The mode matrix was partitioned into a number of submatrices and local feature information contained in each submatrix was extracted as a singular value vector using SVD. The singular value vector matrix corresponding to the current fault state was constructed according to the location of each submatrix. Finally, by training a CNN using singular value vector matrices as inputs, planetary gear fault state identification and classification was achieved. The experimental results confirm that the proposed method can successfully extract local weak feature information and accurately identify different faults. The singular value vector matrices of different fault states have a distinct difference in element size and waveform. The VMD-based partition extraction method is better than ensemble empirical mode decomposition (EEMD), resulting in a higher CNN total recognition rate of 100% with fewer training times (14 times). Further analysis demonstrated that the method can also be applied to the degradation recognition of planetary gears. Thus, the proposed method is an effective feature extraction and fault diagnosis technique for planetary gears. PMID:29751671

Automatic motion and noise artifact detection in Holter ECG data using empirical mode decomposition and statistical approaches.

PubMed

Lee, Jinseok; McManus, David D; Merchant, Sneh; Chon, Ki H

2012-06-01

We present a real-time method for the detection of motion and noise (MN) artifacts, which frequently interferes with accurate rhythm assessment when ECG signals are collected from Holter monitors. Our MN artifact detection approach involves two stages. The first stage involves the use of the first-order intrinsic mode function (F-IMF) from the empirical mode decomposition to isolate the artifacts' dynamics as they are largely concentrated in the higher frequencies. The second stage of our approach uses three statistical measures on the F-IMF time series to look for characteristics of randomness and variability, which are hallmark signatures of MN artifacts: the Shannon entropy, mean, and variance. We then use the receiver-operator characteristics curve on Holter data from 15 healthy subjects to derive threshold values associated with these statistical measures to separate between the clean and MN artifacts' data segments. With threshold values derived from 15 training data sets, we tested our algorithms on 30 additional healthy subjects. Our results show that our algorithms are able to detect the presence of MN artifacts with sensitivity and specificity of 96.63% and 94.73%, respectively. In addition, when we applied our previously developed algorithm for atrial fibrillation (AF) detection on those segments that have been labeled to be free from MN artifacts, the specificity increased from 73.66% to 85.04% without loss of sensitivity (74.48%-74.62%) on six subjects diagnosed with AF. Finally, the computation time was less than 0.2 s using a MATLAB code, indicating that real-time application of the algorithms is possible for Holter monitoring.

Collaborative Research: Process-Resolving Decomposition of the Global Temperature Response to Modes of Low Frequency Variability in a Changing Climate

DOE Office of Scientific and Technical Information (OSTI.GOV)

Deng, Yi

2014-11-24

DOE-GTRC-05596 11/24/2104 Collaborative Research: Process-Resolving Decomposition of the Global Temperature Response to Modes of Low Frequency Variability in a Changing Climate PI: Dr. Yi Deng (PI) School of Earth and Atmospheric Sciences Georgia Institute of Technology 404-385-1821, yi.deng@eas.gatech.edu El Niño-Southern Oscillation (ENSO) and Annular Modes (AMs) represent respectively the most important modes of low frequency variability in the tropical and extratropical circulations. The projection of future changes in the ENSO and AM variability, however, remains highly uncertain with the state-of-the-science climate models. This project conducted a process-resolving, quantitative evaluations of the ENSO and AM variability in the modern reanalysis observationsmore » and in climate model simulations. The goal is to identify and understand the sources of uncertainty and biases in models’ representation of ENSO and AM variability. Using a feedback analysis method originally formulated by one of the collaborative PIs, we partitioned the 3D atmospheric temperature anomalies and surface temperature anomalies associated with ENSO and AM variability into components linked to 1) radiation-related thermodynamic processes such as cloud and water vapor feedbacks, 2) local dynamical processes including convection and turbulent/diffusive energy transfer and 3) non-local dynamical processes such as the horizontal energy transport in the oceans and atmosphere. In the past 4 years, the research conducted at Georgia Tech under the support of this project has led to 15 peer-reviewed publications and 9 conference/workshop presentations. Two graduate students and one postdoctoral fellow also received research training through participating the project activities. This final technical report summarizes key scientific discoveries we made and provides also a list of all publications and conference presentations resulted from research activities at Georgia Tech. The main findings include: 1) the distinctly different roles played by atmospheric dynamical processes in establishing surface temperature response to ENSO at tropics and extratropics (i.e., atmospheric dynamics disperses energy out of tropics during ENSO warm events and modulate surface temperature at mid-, high-latitudes through controlling downward longwave radiation); 2) the representations of ENSO-related temperature response in climate models fail to converge at the process-level particularly over extratropics (i.e., models produce the right temperature responses to ENSO but with wrong reasons); 3) water vapor feedback contributes substantially to the temperature anomalies found over U.S. during different phases of the Northern Annular Mode (NAM), which adds new insight to the traditional picture that cold/warm advective processes are the main drivers of local temperature responses to the NAM; 4) the overall land surface temperature biases in the latest NCAR model (CESM1) are caused by biases in surface albedo while the surface temperature biases over ocean are related to multiple factors including biases in model albedo, cloud and oceanic dynamics, and the temperature biases over different ocean basins are also induced by different process biases. These results provide a detailed guidance for process-level model turning and improvement, and thus contribute directly to the overall goal of reducing model uncertainty in projecting future changes in the Earth’s climate system, especially in the ENSO and AM variability.« less

Influence of Different Forest System Management Practices on Leaf Litter Decomposition Rates, Nutrient Dynamics and the Activity of Ligninolytic Enzymes: A Case Study from Central European Forests

PubMed Central

Schulz, Elke; Schloter, Michael; Buscot, François; Hofrichter, Martin; Krüger, Dirk

2014-01-01

Leaf litter decomposition is the key ecological process that determines the sustainability of managed forest ecosystems, however very few studies hitherto have investigated this process with respect to silvicultural management practices. The aims of the present study were to investigate the effects of forest management practices on leaf litter decomposition rates, nutrient dynamics (C, N, Mg, K, Ca, P) and the activity of ligninolytic enzymes. We approached these questions using a 473 day long litterbag experiment. We found that age-class beech and spruce forests (high forest management intensity) had significantly higher decomposition rates and nutrient release (most nutrients) than unmanaged deciduous forest reserves (P<0.05). The site with near-to-nature forest management (low forest management intensity) exhibited no significant differences in litter decomposition rate, C release, lignin decomposition, and C/N, lignin/N and ligninolytic enzyme patterns compared to the unmanaged deciduous forest reserves, but most nutrient dynamics examined in this study were significantly faster under such near-to-nature forest management practices. Analyzing the activities of ligninolytic enzymes provided evidence that different forest system management practices affect litter decomposition by changing microbial enzyme activities, at least over the investigated time frame of 473 days (laccase, P<0.0001; manganese peroxidase (MnP), P = 0.0260). Our results also indicate that lignin decomposition is the rate limiting step in leaf litter decomposition and that MnP is one of the key oxidative enzymes of litter degradation. We demonstrate here that forest system management practices can significantly affect important ecological processes and services such as decomposition and nutrient cycling. PMID:24699676

Influence of different forest system management practices on leaf litter decomposition rates, nutrient dynamics and the activity of ligninolytic enzymes: a case study from central European forests.

PubMed

Purahong, Witoon; Kapturska, Danuta; Pecyna, Marek J; Schulz, Elke; Schloter, Michael; Buscot, François; Hofrichter, Martin; Krüger, Dirk

2014-01-01

Leaf litter decomposition is the key ecological process that determines the sustainability of managed forest ecosystems, however very few studies hitherto have investigated this process with respect to silvicultural management practices. The aims of the present study were to investigate the effects of forest management practices on leaf litter decomposition rates, nutrient dynamics (C, N, Mg, K, Ca, P) and the activity of ligninolytic enzymes. We approached these questions using a 473 day long litterbag experiment. We found that age-class beech and spruce forests (high forest management intensity) had significantly higher decomposition rates and nutrient release (most nutrients) than unmanaged deciduous forest reserves (P<0.05). The site with near-to-nature forest management (low forest management intensity) exhibited no significant differences in litter decomposition rate, C release, lignin decomposition, and C/N, lignin/N and ligninolytic enzyme patterns compared to the unmanaged deciduous forest reserves, but most nutrient dynamics examined in this study were significantly faster under such near-to-nature forest management practices. Analyzing the activities of ligninolytic enzymes provided evidence that different forest system management practices affect litter decomposition by changing microbial enzyme activities, at least over the investigated time frame of 473 days (laccase, P<0.0001; manganese peroxidase (MnP), P = 0.0260). Our results also indicate that lignin decomposition is the rate limiting step in leaf litter decomposition and that MnP is one of the key oxidative enzymes of litter degradation. We demonstrate here that forest system management practices can significantly affect important ecological processes and services such as decomposition and nutrient cycling.

Evaluating imaging quality between different ghost imaging systems based on the coherent-mode representation

NASA Astrophysics Data System (ADS)

Shen, Qian; Bai, Yanfeng; Shi, Xiaohui; Nan, Suqin; Qu, Lijie; Li, Hengxing; Fu, Xiquan

2017-07-01

The difference in imaging quality between different ghost imaging schemes is studied by using coherent-mode representation of partially coherent fields. It is shown that the difference mainly relies on the distribution changes of the decomposition coefficients of the object imaged when the light source is fixed. For a new-designed imaging scheme, we only need to give the distribution of the decomposition coefficients and compare them with that of the existing imaging system, thus one can predict imaging quality. By choosing several typical ghost imaging systems, we theoretically and experimentally verify our results.

Empirical mode decomposition for analyzing acoustical signals

NASA Technical Reports Server (NTRS)

Huang, Norden E. (Inventor)

2005-01-01

The present invention discloses a computer implemented signal analysis method through the Hilbert-Huang Transformation (HHT) for analyzing acoustical signals, which are assumed to be nonlinear and nonstationary. The Empirical Decomposition Method (EMD) and the Hilbert Spectral Analysis (HSA) are used to obtain the HHT. Essentially, the acoustical signal will be decomposed into the Intrinsic Mode Function Components (IMFs). Once the invention decomposes the acoustic signal into its constituting components, all operations such as analyzing, identifying, and removing unwanted signals can be performed on these components. Upon transforming the IMFs into Hilbert spectrum, the acoustical signal may be compared with other acoustical signals.

Community ecology in 3D: Tensor decomposition reveals spatio-temporal dynamics of large ecological communities.

PubMed

Frelat, Romain; Lindegren, Martin; Denker, Tim Spaanheden; Floeter, Jens; Fock, Heino O; Sguotti, Camilla; Stäbler, Moritz; Otto, Saskia A; Möllmann, Christian

2017-01-01

Understanding spatio-temporal dynamics of biotic communities containing large numbers of species is crucial to guide ecosystem management and conservation efforts. However, traditional approaches usually focus on studying community dynamics either in space or in time, often failing to fully account for interlinked spatio-temporal changes. In this study, we demonstrate and promote the use of tensor decomposition for disentangling spatio-temporal community dynamics in long-term monitoring data. Tensor decomposition builds on traditional multivariate statistics (e.g. Principal Component Analysis) but extends it to multiple dimensions. This extension allows for the synchronized study of multiple ecological variables measured repeatedly in time and space. We applied this comprehensive approach to explore the spatio-temporal dynamics of 65 demersal fish species in the North Sea, a marine ecosystem strongly altered by human activities and climate change. Our case study demonstrates how tensor decomposition can successfully (i) characterize the main spatio-temporal patterns and trends in species abundances, (ii) identify sub-communities of species that share similar spatial distribution and temporal dynamics, and (iii) reveal external drivers of change. Our results revealed a strong spatial structure in fish assemblages persistent over time and linked to differences in depth, primary production and seasonality. Furthermore, we simultaneously characterized important temporal distribution changes related to the low frequency temperature variability inherent in the Atlantic Multidecadal Oscillation. Finally, we identified six major sub-communities composed of species sharing similar spatial distribution patterns and temporal dynamics. Our case study demonstrates the application and benefits of using tensor decomposition for studying complex community data sets usually derived from large-scale monitoring programs.

Effects of defects on thermal decomposition of HMX via ReaxFF molecular dynamics simulations.

PubMed

Zhou, Ting-Ting; Huang, Feng-Lei

2011-01-20

Effects of molecular vacancies on the decomposition mechanisms and reaction dynamics of condensed-phase β-HMX at various temperatures were studied using ReaxFF molecular dynamics simulations. Results show that three primary initial decomposition mechanisms, namely, N-NO(2) bond dissociation, HONO elimination, and concerted ring fission, exist at both high and lower temperatures. The contribution of the three mechanisms to the initial decomposition of HMX is influenced by molecular vacancies, and the effects vary with temperature. At high temperature (2500 K), molecular vacancies remarkably promote N-N bond cleavage and concerted ring breaking but hinder HONO formation. N-N bond dissociation and HONO elimination are two primary competing reaction mechanisms, and the former is dominant in the initial decomposition. Concerted ring breaking of condensed-phase HMX is not favored at high temperature. At lower temperature (1500 K), the most preferential initial decomposition pathway is N-N bond dissociation followed by the formation of NO(3) (O migration), although all three mechanisms are promoted by molecular vacancies. The promotion effect on concerted ring breaking is considerable at lower temperature. Products resulting from concerted ring breaking appear in the defective system but not in the perfect crystal. The mechanism of HONO elimination is less important at lower temperature. We also estimated the reaction rate constant and activation barriers of initial decomposition with different vacancy concentrations. Molecular vacancies accelerate the decomposition of condensed-phase HMX by increasing the reaction rate constant and reducing activation barriers.

Thermal decomposition reactions of cotton fabric treated with piperazine-phosphonates derivatives as a flame retardant

USDA-ARS?s Scientific Manuscript database

There has been a great scientific interest in exploring the great potential of the piperazine-phosphonates in flame retardant (FR) application on cotton fabric by investigating the thermal decomposition of cotton fabric treated with them. This research tries to understand the mode of action of the t...

Mineral cycling in soil and litter arthropod food chains. Three-year progress report, February 1, 1984-January 31, 1987

DOE Office of Scientific and Technical Information (OSTI.GOV)

Crossley, D.A. Jr.

1986-08-29

This report summarizes progress in a three-year research project on the influence of soil arthropods (mites, collembolans, insects, millipedes and others) upon decomposition rates and nutrient dynamics in decaying vegetable matter. Research has concentrated on two aspects of elemental dynamics in decomposing organic matter: Effects of arthropods on rates of decomposition and nutrient loss (mineralization of carbon and other elements), and arthropod stimulation of microbial immobilization of nutrient elements during decomposition.

Incremental dynamical downscaling for probabilistic analysis based on multiple GCM projections

NASA Astrophysics Data System (ADS)

Wakazuki, Y.

2015-12-01

A dynamical downscaling method for probabilistic regional scale climate change projections was developed to cover an uncertainty of multiple general circulation model (GCM) climate simulations. The climatological increments (future minus present climate states) estimated by GCM simulation results were statistically analyzed using the singular vector decomposition. Both positive and negative perturbations from the ensemble mean with the magnitudes of their standard deviations were extracted and were added to the ensemble mean of the climatological increments. The analyzed multiple modal increments were utilized to create multiple modal lateral boundary conditions for the future climate regional climate model (RCM) simulations by adding to an objective analysis data. This data handling is regarded to be an advanced method of the pseudo-global-warming (PGW) method previously developed by Kimura and Kitoh (2007). The incremental handling for GCM simulations realized approximated probabilistic climate change projections with the smaller number of RCM simulations. Three values of a climatological variable simulated by RCMs for a mode were used to estimate the response to the perturbation of the mode. For the probabilistic analysis, climatological variables of RCMs were assumed to show linear response to the multiple modal perturbations, although the non-linearity was seen for local scale rainfall. Probability of temperature was able to be estimated within two modes perturbation simulations, where the number of RCM simulations for the future climate is five. On the other hand, local scale rainfalls needed four modes simulations, where the number of the RCM simulations is nine. The probabilistic method is expected to be used for regional scale climate change impact assessment in the future.

Detection and reconstruction of large scale flow structures in a river by means of empirical mode decomposition combined with Hilbert transform

NASA Astrophysics Data System (ADS)

Franca, Mário J.; Lemmin, Ulrich

2014-05-01

The occurrence of large scale flow structures (LSFS) coherently organized throughout the flow depth has been reported in field and laboratory experiments of flows over gravel beds, especially under low relative submergence conditions. In these, the instantaneous velocity is synchronized over the whole vertical profile oscillating at a low frequency above or below the time-averaged value. The detection of large scale coherently organized regions in the flow field is often difficult since it requires detailed simultaneous observations of the flow velocities at several levels. The present research avoids the detection problem by using an Acoustic Doppler Velocity Profiler (ADVP), which permits measuring three-dimensional velocities quasi-simultaneously over the full water column. Empirical mode decomposition (EMD) combined with the application of the Hilbert transform is then applied to the instantaneous velocity data to detect and isolate LSFS. The present research was carried out in a Swiss river with low relative submergence of 2.9, herein defined as h/D50, (where h is the mean flow depth and D50 the bed grain size diameter for which 50% of the grains have smaller diameters). 3D ADVP instantaneous velocity measurements were made on a 3x5 rectangular horizontal grid (x-y). Fifteen velocity profiles were equally spaced in the spanwise direction with a distance of 10 cm, and in the streamwise direction with a distance of 15 cm. The vertical resolution of the measurements is roughly 0.5 cm. A measuring grid covering a 3D control volume was defined. The instantaneous velocity profiles were measured for 3.5 min with a sampling frequency of 26 Hz. Oscillating LSFS are detected and isolated in the instantaneous velocity signal of the 15 measured profiles. Their 3D cycle geometry is reconstructed and investigated through phase averaging based on the identification of the instantaneous signal phase (related to the Hilbert transform) applied to the original raw signal. Results for all the profiles are consistent and indicate clearly the presence of LSFS throughout the flow depth with impact on the three components of the velocity profile and on the bed friction velocity. A high correlation of the movement is found throughout the flow depth, thus corroborating the hypothesis of large-scale coherent motion evolving over the whole water depth. These latter are characterized in terms of period, horizontal scale and geometry. The high spatial and temporal resolution of our ADVP was crucial for obtaining comprehensive results on coherent structures dynamics. EMD combined with the Hilbert transform have previously been successfully applied to geophysical flow studies. Here we show that this method can also be used for the analysis of river dynamics. In particular, we demonstrate that a clean, well-behaved intrinsic mode function can be obtained from a noisy velocity time series that allowed a precise determination of the vertical structure of the coherent structures. The phase unwrapping of the UMR and the identification of the phase related velocity components brings new insight into the flow dynamics Research supported by the Swiss National Science Foundation (2000-063818). KEY WORDS: large scale flow structures (LSFS); gravel-bed rivers; empirical mode decomposition; Hilbert transform

Reduced dynamical model of the vibrations of a metal plate

NASA Astrophysics Data System (ADS)

Moreno, D.; Barrientos, Bernardino; Perez-Lopez, Carlos; Mendoza-Santoyo, Fernando; Guerrero, J. A.; Funes, M.

2005-02-01

The Proper Orthogonal Decomposition (POD) method is applied to the vibrations analysis of a metal plate. The data obtained from the metal plate under vibrations were measured with a laser vibrometer. The metal plate was subject to vibrations with an electrodynamical shaker in a range of frequencies from 100 to 5000 Hz. The deformation measurements were taken on a quarter of the plate in a rectangular grid of 7 x 8 points. The plate deformation measurements were used to calculate the eigenfunctions and the eigenvalues. It was found that a large fraction of the total energy of the deformation is contained within the first six POD modes. The essential features of the deformation are thus described by only the six first eigenfunctions. A reduced order model for the dynamical behavior is then constructed using Galerkin projection of the equation of motion for the vertical displacement of a plate.

Investigation of shock-induced chemical decomposition of sensitized nitromethane through time-resolved Raman spectroscopy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pangilinan, G.I.; Constantinou, C.P.; Gruzdkov, Y.A.

1996-07-01

Molecular processes associated with shock induced chemical decomposition of a mixture of nitromethane with ethylenediamine (0.1 wt%) are examined using time-resolved, Raman scattering. When shocked by stepwise loading to 14.2 GPa pressure, changes in the nitromethane vibrational modes and the spectral background characterize the onset of reaction. The CN stretch mode softens and disappears even as the NO{sub 2} and CH{sub 3} stretch modes, though modified, retain their identities. The shape and intensity of the spectral background also shows changes characteristic of reaction. Changes in the background, which are observed even at lower peak pressures of 11.4 GPa, are assignedmore » to luminescence from reaction intermediates. The implications of these results to various molecular models of sensitization are discussed.« less

Decomposition drives convergence of forest litter nutrient stoichiometry following phosphorus addition

USGS Publications Warehouse

van Huysen, Tiff L.; Perakis, Steven; Harmon, Mark E.

2016-01-01

We conclude that litter P concentrations and to some extent soil P may influence litter nutrient dynamics during decomposition, resulting in a convergence of element ratios that reflect the balance of substrate decomposition and microbial nutrient stoichiometry.

OMA analysis of a launcher under operational conditions with time-varying properties

NASA Astrophysics Data System (ADS)

Eugeni, M.; Coppotelli, G.; Mastroddi, F.; Gaudenzi, P.; Muller, S.; Troclet, B.

2018-05-01

The objective of this paper is the investigation of the capability of operational modal analysis approaches to deal with time-varying system in the low-frequency domain. Specifically, the problem of the identification of the dynamic properties of a launch vehicle, working under actual operative conditions, is studied. Two OMA methods are considered: the frequency-domain decomposition and the Hilbert transform method. It is demonstrated that both OMA approaches allow the time-tracking of modal parameters, namely, natural frequencies, damping ratios, and mode shapes, from the response accelerations only recorded during actual flight tests of a launcher characterized by a large mass variation due to fuel burning typical of the first phase of the flight.

LLNL demonstration of liquid gun propellant destruction in a 0.1 gallon per minute scale reactor

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cena, R.J.; Thorsness, C.B.; Coburn, T.T.

1994-06-01

The Lawrence Livermore National Laboratory (LLNL) has built and operated a pilot plant for processing oil shale using recirculating hot solids. This pilot plant, was adapted in 1993 to demonstrate the feasibility of decomposing a liquid gun propellant (LGP), LP XM46, a mixture of 76% HAN (NH{sub 3}OHNO{sub 3}) and 24% TEAN (HOCH{sub 2}CH{sub 2}){sub 3} NHNO{sub 3} diluted 1:3 in water. In the Livermore process, the LPG is thermally treated in a moving packed bed of ceramic spheres, where TEAN and HAN decompose, forming a suite of gases including: methane, carbon monoxide, oxygen, nitrogen oxides, ammonia and molecular nitrogen.more » The ceramic spheres are circulated and heated, providing the energy required for thermal decomposition. The authors performed an extended one day (8 hour) test of the solids recirculation system, with continuous injection of approximately 0.1 gal/min of LGP, diluted 1:3 in water, for a period of eight hours. The apparatus operated smoothly over the course of the eight hour run during which 144 kg of solution was processed, containing 36 kg of LGP. Continuous on-line gas analysis was invaluable in tracking the progress of the experiment and quantifying the decomposition products. The reactor was operated in two modes, a {open_quotes}Pyrolysis{close_quotes} mode, where decomposition products were removed from the moving bed reactor exit, passing through condensers to a flare, and in a {open_quotes}Combustion{close_quotes} mode, where the products were oxidized in air lift pipe prior to exiting the system. In the {open_quotes}Pyrolysis{close_quotes} mode, driver gases were recycled producing a small, concentrated stream of decomposition products. In the {open_quotes}Combustion mode{close_quotes}, the driver gases were not recycled, resulting in 40 times higher gas flow rates and correspondingly lower concentrations of nitrogen bearing gases.« less

Decompositions of large-scale biological systems based on dynamical properties.

PubMed

Soranzo, Nicola; Ramezani, Fahimeh; Iacono, Giovanni; Altafini, Claudio

2012-01-01

Given a large-scale biological network represented as an influence graph, in this article we investigate possible decompositions of the network aimed at highlighting specific dynamical properties. The first decomposition we study consists in finding a maximal directed acyclic subgraph of the network, which dynamically corresponds to searching for a maximal open-loop subsystem of the given system. Another dynamical property investigated is strong monotonicity. We propose two methods to deal with this property, both aimed at decomposing the system into strongly monotone subsystems, but with different structural characteristics: one method tends to produce a single large strongly monotone component, while the other typically generates a set of smaller disjoint strongly monotone subsystems. Original heuristics for the methods investigated are described in the article. altafini@sissa.it

Dynamic Load Balancing Based on Constrained K-D Tree Decomposition for Parallel Particle Tracing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Jiang; Guo, Hanqi; Yuan, Xiaoru

Particle tracing is a fundamental technique in flow field data visualization. In this work, we present a novel dynamic load balancing method for parallel particle tracing. Specifically, we employ a constrained k-d tree decomposition approach to dynamically redistribute tasks among processes. Each process is initially assigned a regularly partitioned block along with duplicated ghost layer under the memory limit. During particle tracing, the k-d tree decomposition is dynamically performed by constraining the cutting planes in the overlap range of duplicated data. This ensures that each process is reassigned particles as even as possible, and on the other hand the newmore » assigned particles for a process always locate in its block. Result shows good load balance and high efficiency of our method.« less

A new multivariate empirical mode decomposition method for improving the performance of SSVEP-based brain-computer interface

NASA Astrophysics Data System (ADS)

Chen, Yi-Feng; Atal, Kiran; Xie, Sheng-Quan; Liu, Quan

2017-08-01

Objective. Accurate and efficient detection of steady-state visual evoked potentials (SSVEP) in electroencephalogram (EEG) is essential for the related brain-computer interface (BCI) applications. Approach. Although the canonical correlation analysis (CCA) has been applied extensively and successfully to SSVEP recognition, the spontaneous EEG activities and artifacts that often occur during data recording can deteriorate the recognition performance. Therefore, it is meaningful to extract a few frequency sub-bands of interest to avoid or reduce the influence of unrelated brain activity and artifacts. This paper presents an improved method to detect the frequency component associated with SSVEP using multivariate empirical mode decomposition (MEMD) and CCA (MEMD-CCA). EEG signals from nine healthy volunteers were recorded to evaluate the performance of the proposed method for SSVEP recognition. Main results. We compared our method with CCA and temporally local multivariate synchronization index (TMSI). The results suggest that the MEMD-CCA achieved significantly higher accuracy in contrast to standard CCA and TMSI. It gave the improvements of 1.34%, 3.11%, 3.33%, 10.45%, 15.78%, 18.45%, 15.00% and 14.22% on average over CCA at time windows from 0.5 s to 5 s and 0.55%, 1.56%, 7.78%, 14.67%, 13.67%, 7.33% and 7.78% over TMSI from 0.75 s to 5 s. The method outperformed the filter-based decomposition (FB), empirical mode decomposition (EMD) and wavelet decomposition (WT) based CCA for SSVEP recognition. Significance. The results demonstrate the ability of our proposed MEMD-CCA to improve the performance of SSVEP-based BCI.

Chemical dynamics simulations of X- + CH3Y → XCH3 + Y- gas-phase S(N)2 nucleophilic substitution reactions. Nonstatistical dynamics and nontraditional reaction mechanisms.

PubMed

Manikandan, Paranjothy; Zhang, Jiaxu; Hase, William L

2012-03-29

Extensive classical chemical dynamics simulations of gas-phase X(-) + CH(3)Y → XCH(3) + Y(-) S(N)2 nucleophilic substitution reactions are reviewed and discussed and compared with experimental measurements and predictions of theoretical models. The primary emphasis is on reactions for which X and Y are halogen atoms. Both reactions with the traditional potential energy surface (PES), which include pre- and postreaction potential energy minima and a central barrier, and reactions with nontraditional PESs are considered. These S(N)2 reactions exhibit important nonstatistical atomic-level dynamics. The X(-) + CH(3)Y → X(-)---CH(3)Y association rate constant is less than the capture model as a result of inefficient energy transfer from X(-)+ CH(3)Y relative translation to CH(3)Y rotation and vibration. There is weak coupling between the low-frequency intermolecular modes of the X(-)---CH(3)Y complex and higher frequency CH(3)Y intramolecular modes, resulting in non-RRKM kinetics for X(-)---CH(3)Y unimolecular decomposition. Recrossings of the [X--CH(3)--Y](-) central barrier is important. As a result of the above dynamics, the relative translational energy and temperature dependencies of the S(N)2 rate constants are not accurately given by statistical theory. The nonstatistical dynamics results in nonstatistical partitioning of the available energy to XCH(3) +Y(-) reaction products. Besides the indirect, complex forming atomic-level mechanism for the S(N)2 reaction, direct mechanisms promoted by X(-) + CH(3)Y relative translational or CH(3)Y vibrational excitation are possible, e.g., the roundabout mechanism.

Vibration fatigue using modal decomposition

NASA Astrophysics Data System (ADS)

Mršnik, Matjaž; Slavič, Janko; Boltežar, Miha

2018-01-01

Vibration-fatigue analysis deals with the material fatigue of flexible structures operating close to natural frequencies. Based on the uniaxial stress response, calculated in the frequency domain, the high-cycle fatigue model using the S-N curve material data and the Palmgren-Miner hypothesis of damage accumulation is applied. The multiaxial criterion is used to obtain the equivalent uniaxial stress response followed by the spectral moment approach to the cycle-amplitude probability density estimation. The vibration-fatigue analysis relates the fatigue analysis in the frequency domain to the structural dynamics. However, once the stress response within a node is obtained, the physical model of the structure dictating that response is discarded and does not propagate through the fatigue-analysis procedure. The structural model can be used to evaluate how specific dynamic properties (e.g., damping, modal shapes) affect the damage intensity. A new approach based on modal decomposition is presented in this research that directly links the fatigue-damage intensity with the dynamic properties of the system. It thus offers a valuable insight into how different modes of vibration contribute to the total damage to the material. A numerical study was performed showing good agreement between results obtained using the newly presented approach with those obtained using the classical method, especially with regards to the distribution of damage intensity and critical point location. The presented approach also offers orders of magnitude faster calculation in comparison with the conventional procedure. Furthermore, it can be applied in a straightforward way to strain experimental modal analysis results, taking advantage of experimentally measured strains.

Thermal decomposition of condensed-phase nitromethane from molecular dynamics from ReaxFF reactive dynamics.

PubMed

Han, Si-ping; van Duin, Adri C T; Goddard, William A; Strachan, Alejandro

2011-05-26

We studied the thermal decomposition and subsequent reaction of the energetic material nitromethane (CH(3)NO(2)) using molecular dynamics with ReaxFF, a first principles-based reactive force field. We characterize the chemistry of liquid and solid nitromethane at high temperatures (2000-3000 K) and density 1.97 g/cm(3) for times up to 200 ps. At T = 3000 K the first reaction in the decomposition of nitromethane is an intermolecular proton transfer leading to CH(3)NOOH and CH(2)NO(2). For lower temperatures (T = 2500 and 2000 K) the first reaction during decomposition is often an isomerization reaction involving the scission of the C-N bond the formation of a C-O bond to form methyl nitrate (CH(3)ONO). Also at very early times we observe intramolecular proton transfer events. The main product of these reactions is H(2)O which starts forming following those initiation steps. The appearance of H(2)O marks the beginning of the exothermic chemistry. Recent quantum-mechanics-based molecular dynamics simulations on the chemical reactions and time scales for decomposition of a crystalline sample heated to T = 3000 K for a few picoseconds are in excellent agreement with our results, providing an important, direct validation of ReaxFF.

An ab initio molecular dynamics study of thermal decomposition of 3,6-di(azido)-1,2,4,5-tetrazine.

PubMed

Wu, Qiong; Zhu, Weihua; Xiao, Heming

2014-10-21

Ab initio molecular dynamics simulations were performed to study the thermal decomposition of isolated and crystal 3,6-di(azido)-1,2,4,5-tetrazine (DiAT). During unimolecular decomposition, the three different initiation mechanisms were observed to be N-N2 cleavage, ring opening, and isomerization, respectively. The preferential initial decomposition step is the homolysis of the N-N2 bond in the azido group. The release mechanisms of nitrogen gas are found to be very different in the early and later decomposition stages of crystal DiAT. In the early decomposition, DiAT decomposes very fast and drastically without forming any stable long-chains or heterocyclic clusters, and most of the nitrogen gases are released through rapid rupture of nitrogen-nitrogen and carbon-nitrogen bonds. But in the later decomposition stage, the release of nitrogen gas is inhibited due to low mobility, long distance from each other, and strong carbon-nitrogen bonds. To overcome the obstacles, the nitrogen gases are released through slow formation and disintegration of polycyclic networks. Our simulations suggest a new decomposition mechanism for the organic polyazido initial explosive at the atomistic level.

Effects of experimental throughfall reduction and soil warming on fine root biomass and its decomposition in a warm temperate oak forest.

PubMed

Liu, Yanchun; Liu, Shirong; Wan, Shiqiang; Wang, Jingxin; Wang, Hui; Liu, Kuan

2017-01-01

Fine root dynamics play a critical role in regulating carbon (C) cycling in terrestrial ecosystems. Examining responses of fine root biomass and its decomposition to altered precipitation pattern and climate warming is crucial to understand terrestrial C dynamics and its feedback to climate change. Fine root biomass and its decomposition rate were investigated in a warm temperate oak forest through a field manipulation experiment with throughfall reduction and soil warming conducted. Throughfall reduction significantly interacted with soil warming in affecting fine root biomass and its decomposition. Throughfall reduction substantially increased fine root biomass and its decomposition in unheated plots, but negative effects occurred in warmed plots. Soil warming significantly enhanced fine root biomass and its decomposition under ambient precipitation, but the opposite effects exhibited under throughfall reduction. Different responses in fine root biomass among different treatments could be largely attributed to soil total nitrogen (N), while fine root decomposition rate was more depended on microbial biomass C and N. Our observations indicate that decreased precipitation may offset the positive effect of soil warming on fine root biomass and decomposition. Copyright © 2016 Elsevier B.V. All rights reserved.

Community ecology in 3D: Tensor decomposition reveals spatio-temporal dynamics of large ecological communities

PubMed Central

Lindegren, Martin; Denker, Tim Spaanheden; Floeter, Jens; Fock, Heino O.; Sguotti, Camilla; Stäbler, Moritz; Otto, Saskia A.; Möllmann, Christian

2017-01-01

Understanding spatio-temporal dynamics of biotic communities containing large numbers of species is crucial to guide ecosystem management and conservation efforts. However, traditional approaches usually focus on studying community dynamics either in space or in time, often failing to fully account for interlinked spatio-temporal changes. In this study, we demonstrate and promote the use of tensor decomposition for disentangling spatio-temporal community dynamics in long-term monitoring data. Tensor decomposition builds on traditional multivariate statistics (e.g. Principal Component Analysis) but extends it to multiple dimensions. This extension allows for the synchronized study of multiple ecological variables measured repeatedly in time and space. We applied this comprehensive approach to explore the spatio-temporal dynamics of 65 demersal fish species in the North Sea, a marine ecosystem strongly altered by human activities and climate change. Our case study demonstrates how tensor decomposition can successfully (i) characterize the main spatio-temporal patterns and trends in species abundances, (ii) identify sub-communities of species that share similar spatial distribution and temporal dynamics, and (iii) reveal external drivers of change. Our results revealed a strong spatial structure in fish assemblages persistent over time and linked to differences in depth, primary production and seasonality. Furthermore, we simultaneously characterized important temporal distribution changes related to the low frequency temperature variability inherent in the Atlantic Multidecadal Oscillation. Finally, we identified six major sub-communities composed of species sharing similar spatial distribution patterns and temporal dynamics. Our case study demonstrates the application and benefits of using tensor decomposition for studying complex community data sets usually derived from large-scale monitoring programs. PMID:29136658

Dynamics of a neural system with a multiscale architecture

PubMed Central

Breakspear, Michael; Stam, Cornelis J

2005-01-01

The architecture of the brain is characterized by a modular organization repeated across a hierarchy of spatial scales—neurons, minicolumns, cortical columns, functional brain regions, and so on. It is important to consider that the processes governing neural dynamics at any given scale are not only determined by the behaviour of other neural structures at that scale, but also by the emergent behaviour of smaller scales, and the constraining influence of activity at larger scales. In this paper, we introduce a theoretical framework for neural systems in which the dynamics are nested within a multiscale architecture. In essence, the dynamics at each scale are determined by a coupled ensemble of nonlinear oscillators, which embody the principle scale-specific neurobiological processes. The dynamics at larger scales are ‘slaved’ to the emergent behaviour of smaller scales through a coupling function that depends on a multiscale wavelet decomposition. The approach is first explicated mathematically. Numerical examples are then given to illustrate phenomena such as between-scale bifurcations, and how synchronization in small-scale structures influences the dynamics in larger structures in an intuitive manner that cannot be captured by existing modelling approaches. A framework for relating the dynamical behaviour of the system to measured observables is presented and further extensions to capture wave phenomena and mode coupling are suggested. PMID:16087448

Human versus animal: contrasting decomposition dynamics of mammalian analogues in experimental taphonomy.

PubMed

Stokes, Kathryn L; Forbes, Shari L; Tibbett, Mark

2013-05-01

Taphonomic studies regularly employ animal analogues for human decomposition due to ethical restrictions relating to the use of human tissue. However, the validity of using animal analogues in soil decomposition studies is still questioned. This study compared the decomposition of skeletal muscle tissues (SMTs) from human (Homo sapiens), pork (Sus scrofa), beef (Bos taurus), and lamb (Ovis aries) interred in soil microcosms. Fixed interval samples were collected from the SMT for microbial activity and mass tissue loss determination; samples were also taken from the underlying soil for pH, electrical conductivity, and nutrient (potassium, phosphate, ammonium, and nitrate) analysis. The overall patterns of nutrient fluxes and chemical changes in nonhuman SMT and the underlying soil followed that of human SMT. Ovine tissue was the most similar to human tissue in many of the measured parameters. Although no single analogue was a precise predictor of human decomposition in soil, all models offered close approximations in decomposition dynamics. © 2013 American Academy of Forensic Sciences.

Influence of density and environmental factors on decomposition kinetics of amorphous polylactide - Reactive molecular dynamics studies.

PubMed

Mlyniec, A; Ekiert, M; Morawska-Chochol, A; Uhl, T

2016-06-01

In this work, we investigate the influence of the surrounding environment and the initial density on the decomposition kinetics of polylactide (PLA). The decomposition of the amorphous PLA was investigated by means of reactive molecular dynamics simulations. A computational model simulates the decomposition of PLA polymer inside the bulk, due to the assumed lack of removal of reaction products from the polymer matrix. We tracked the temperature dependency of the water and carbon monoxide production to extract the activation energy of thermal decomposition of PLA. We found that an increased density results in decreased activation energy of decomposition by about 50%. Moreover, initiation of decomposition of the amorphous PLA is followed by a rapid decline in activation energy caused by reaction products which accelerates the hydrolysis of esters. The addition of water molecules decreases initial energy of activation as well as accelerates the decomposition process. Additionally, we have investigated the dependency of density on external loading. Comparison of pressures needed to obtain assumed densities shows that this relationship is bilinear and the slope changes around a density equal to 1.3g/cm(3). The conducted analyses provide an insight into the thermal decomposition process of the amorphous phase of PLA, which is particularly susceptible to decomposition in amorphous and semi-crystalline PLA polymers. Copyright © 2016 Elsevier Inc. All rights reserved.

Multiscale Characterization of PM2.5 in Southern Taiwan based on Noise-assisted Multivariate Empirical Mode Decomposition and Time-dependent Intrinsic Correlation

NASA Astrophysics Data System (ADS)

Hsiao, Y. R.; Tsai, C.

2017-12-01

As the WHO Air Quality Guideline indicates, ambient air pollution exposes world populations under threat of fatal symptoms (e.g. heart disease, lung cancer, asthma etc.), raising concerns of air pollution sources and relative factors. This study presents a novel approach to investigating the multiscale variations of PM2.5 in southern Taiwan over the past decade, with four meteorological influencing factors (Temperature, relative humidity, precipitation and wind speed),based on Noise-assisted Multivariate Empirical Mode Decomposition(NAMEMD) algorithm, Hilbert Spectral Analysis(HSA) and Time-dependent Intrinsic Correlation(TDIC) method. NAMEMD algorithm is a fully data-driven approach designed for nonlinear and nonstationary multivariate signals, and is performed to decompose multivariate signals into a collection of channels of Intrinsic Mode Functions (IMFs). TDIC method is an EMD-based method using a set of sliding window sizes to quantify localized correlation coefficients for multiscale signals. With the alignment property and quasi-dyadic filter bank of NAMEMD algorithm, one is able to produce same number of IMFs for all variables and estimates the cross correlation in a more accurate way. The performance of spectral representation of NAMEMD-HSA method is compared with Complementary Empirical Mode Decomposition/ Hilbert Spectral Analysis (CEEMD-HSA) and Wavelet Analysis. The nature of NAMAMD-based TDICC analysis is then compared with CEEMD-based TDIC analysis and the traditional correlation analysis.

Resting state networks in empirical and simulated dynamic functional connectivity.

PubMed

Glomb, Katharina; Ponce-Alvarez, Adrián; Gilson, Matthieu; Ritter, Petra; Deco, Gustavo

2017-10-01

It is well-established that patterns of functional connectivity (FC) - measures of correlated activity between pairs of voxels or regions observed in the human brain using neuroimaging - are robustly expressed in spontaneous activity during rest. These patterns are not static, but exhibit complex spatio-temporal dynamics. Over the last years, a multitude of methods have been proposed to reveal these dynamics on the level of the whole brain. One finding is that the brain transitions through different FC configurations over time, and substantial effort has been put into characterizing these configurations. However, the dynamics governing these transitions are more elusive, specifically, the contribution of stationary vs. non-stationary dynamics is an active field of inquiry. In this study, we use a whole-brain approach, considering FC dynamics between 66 ROIs covering the entire cortex. We combine an innovative dimensionality reduction technique, tensor decomposition, with a mean field model which possesses stationary dynamics. It has been shown to explain resting state FC averaged over time and multiple subjects, however, this average FC summarizes the spatial distribution of correlations while hiding their temporal dynamics. First, we apply tensor decomposition to resting state scans from 24 healthy controls in order to characterize spatio-temporal dynamics present in the data. We simultaneously utilize temporal and spatial information by creating tensors that are subsequently decomposed into sets of brain regions ("communities") that share similar temporal dynamics, and their associated time courses. The tensors contain pairwise FC computed inside of overlapping sliding windows. Communities are discovered by clustering features pooled from all subjects, thereby ensuring that they generalize. We find that, on the group level, the data give rise to four distinct communities that resemble known resting state networks (RSNs): default mode network, visual network, control networks, and somatomotor network. Second, we simulate data with our stationary mean field model whose nodes are connected according to results from DTI and fiber tracking. In this model, all spatio-temporal structure is due to noisy fluctuations around the average FC. We analyze the simulated data in the same way as the empirical data in order to determine whether stationary dynamics can explain the emergence of distinct FC patterns (RSNs) which have their own time courses. We find that this is the case for all four networks using the spatio-temporal information revealed by tensor decomposition if nodes in the simulation are connected according to model-based effective connectivity. Furthermore, we find that these results require only a small part of the FC values, namely the highest values that occur across time and ROI pair. Our findings show that stationary dynamics can account for the emergence of RSNs. We provide an innovative method that does not make strong assumptions about the underlying data and is generally applicable to resting state or task data from different subject populations. Copyright © 2017 Elsevier Inc. All rights reserved.

A technique for plasma velocity-space cross-correlation

NASA Astrophysics Data System (ADS)

Mattingly, Sean; Skiff, Fred

2018-05-01

An advance in experimental plasma diagnostics is presented and used to make the first measurement of a plasma velocity-space cross-correlation matrix. The velocity space correlation function can detect collective fluctuations of plasmas through a localized measurement. An empirical decomposition, singular value decomposition, is applied to this Hermitian matrix in order to obtain the plasma fluctuation eigenmode structure on the ion distribution function. A basic theory is introduced and compared to the modes obtained by the experiment. A full characterization of these modes is left for future work, but an outline of this endeavor is provided. Finally, the requirements for this experimental technique in other plasma regimes are discussed.

A New Approach of evaluating the damage in simply-supported reinforced concrete beam by Local mean decomposition (LMD)

NASA Astrophysics Data System (ADS)

Zhang, Xuebing; Liu, Ning; Xi, Jiaxin; Zhang, Yunqi; Zhang, Wenchun; Yang, Peipei

2017-08-01

How to analyze the nonstationary response signals and obtain vibration characters is extremely important in the vibration-based structural diagnosis methods. In this work, we introduce a more reasonable time-frequency decomposition method termed local mean decomposition (LMD) to instead the widely-used empirical mode decomposition (EMD). By employing the LMD method, one can derive a group of component signals, each of which is more stationary, and then analyze the vibration state and make the assessment of structural damage of a construction or building. We illustrated the effectiveness of LMD by a synthetic data and an experimental data recorded in a simply-supported reinforced concrete beam. Then based on the decomposition results, an elementary method of damage diagnosis was proposed.

On the decomposition of a dynamical system into non-interacting subsystems.

NASA Technical Reports Server (NTRS)

Rosen, R.

1972-01-01

It is shown that, under rather general conditions, it is possible to formally decompose the dynamics of an n-dimensional dynamical system into a number of non-interacting subsystems. It is shown that these decompositions are in general not simply related to the kinds of observational procedures in terms of which the original state variables of the system are defined. Some consequences of this construction for reductionism in biology are discussed.

Mode Decomposition Methods for Soil Moisture Prediction

NASA Astrophysics Data System (ADS)

Jana, R. B.; Efendiev, Y. R.; Mohanty, B.

2014-12-01

Lack of reliable, well-distributed, long-term datasets for model validation is a bottle-neck for most exercises in soil moisture analysis and prediction. Understanding what factors drive soil hydrological processes at different scales and their variability is very critical to further our ability to model the various components of the hydrologic cycle more accurately. For this, a comprehensive dataset with measurements across scales is very necessary. Intensive fine-resolution sampling of soil moisture over extended periods of time is financially and logistically prohibitive. Installation of a few long term monitoring stations is also expensive, and needs to be situated at critical locations. The concept of Time Stable Locations has been in use for some time now to find locations that reflect the mean values for the soil moisture across the watershed under all wetness conditions. However, the soil moisture variability across the watershed is lost when measuring at only time stable locations. We present here a study using techniques such as Dynamic Mode Decomposition (DMD) and Discrete Empirical Interpolation Method (DEIM) that extends the concept of time stable locations to arrive at locations that provide not simply the average soil moisture values for the watershed, but also those that can help re-capture the dynamics across all locations in the watershed. As with the time stability, the initial analysis is dependent on an intensive sampling history. The DMD/DEIM method is an application of model reduction techniques for non-linearly related measurements. Using this technique, we are able to determine the number of sampling points that would be required for a given accuracy of prediction across the watershed, and the location of those points. Locations with higher energetics in the basis domain are chosen first. We present case studies across watersheds in the US and India. The technique can be applied to other hydro-climates easily.

Analysis of Decomposition for Structure I Methane Hydrate by Molecular Dynamics Simulation

NASA Astrophysics Data System (ADS)

Wei, Na; Sun, Wan-Tong; Meng, Ying-Feng; Liu, An-Qi; Zhou, Shou-Wei; Guo, Ping; Fu, Qiang; Lv, Xin

2018-05-01

Under multi-nodes of temperatures and pressures, microscopic decomposition mechanisms of structure I methane hydrate in contact with bulk water molecules have been studied through LAMMPS software by molecular dynamics simulation. Simulation system consists of 482 methane molecules in hydrate and 3027 randomly distributed bulk water molecules. Through analyses of simulation results, decomposition number of hydrate cages, density of methane molecules, radial distribution function for oxygen atoms, mean square displacement and coefficient of diffusion of methane molecules have been studied. A significant result shows that structure I methane hydrate decomposes from hydrate-bulk water interface to hydrate interior. As temperature rises and pressure drops, the stabilization of hydrate will weaken, decomposition extent will go deep, and mean square displacement and coefficient of diffusion of methane molecules will increase. The studies can provide important meanings for the microscopic decomposition mechanisms analyses of methane hydrate.

Interactions of tissue and fertilizer nitrogen on decomposition dynamics of lignin-rich conifer litter

USGS Publications Warehouse

Perakis, Steven S.; Matkins, Joselin J.; Hibbs, David E.

2012-01-01

High tissue nitrogen (N) accelerates decomposition of high-quality leaf litter in the early phases of mass loss, but the influence of initial tissue N variation on the decomposition of lignin-rich litter is less resolved. Because environmental changes such as atmospheric N deposition and elevated CO2 can alter tissue N levels within species more rapidly than they alter the species composition of ecosystems, it is important to consider how within-species variation in tissue N may shape litter decomposition and associated N dynamics. Douglas-fir (Pseudotsuga menziesii ) is a widespread lignin-rich conifer that dominates forests of high carbon (C) storage across western North America, and displays wide variation in tissue and litter N that reflects landscape variation in soil N. We collected eight unique Douglas-fir litter sources that spanned a two-fold range in initial N concentrations (0.67–1.31%) with a narrow range of lignin (29–35%), and examined relationships between initial litter chemistry, decomposition, and N dynamics in both ambient and N fertilized plots at four sites over 3 yr. High initial litter N slowed decomposition rates in both early (0.67 yr) and late (3 yr) stages in unfertilized plots. Applications of N fertilizer to litters accelerated early-stage decomposition, but slowed late-stage decomposition, and most strongly affected low-N litters, which equalized decomposition rates across litters regardless of initial N concentrations. Decomposition of N-fertilized litters correlated positively with initial litter manganese (Mn) concentrations, with litter Mn variation reflecting faster turnover of canopy foliage in high N sites, producing younger litterfall with high N and low Mn. Although both internal and external N inhibited decomposition at 3 yr, most litters exhibited net N immobilization, with strongest immobilization in low-N litter and in N-fertilized plots. Our observation for lignin-rich litter that high initial N can slow decomposition yet accelerate N release differs from findings where litter quality variation across species promotes coupled C and N release during decomposition. We suggest reevaluation of ecosystem models and projected global change effects to account for a potential decoupling of ecosystem C and N feedbacks through litter decomposition in lignin-rich conifer forests.

Land-use legacies regulate decomposition dynamics following bioenergy crop conversion

DOE PAGES

Kallenbach, Cynthia M.; Stuart Grandy, A.

2014-07-14

Land-use conversion into bioenergy crop production can alter litter decomposition processes tightly coupled to soil carbon and nutrient dynamics. Yet, litter decomposition has been poorly described in bioenergy production systems, especially following land-use conversion. Predicting decomposition dynamics in postconversion bioenergy production systems is challenging because of the combined influence of land-use legacies with current management and litter quality. To evaluate how land-use legacies interact with current bioenergy crop management to influence litter decomposition in different litter types, we conducted a landscape-scale litterbag decomposition experiment. We proposed land-use legacies regulate decomposition, but their effects are weakened under higher quality litter andmore » when current land use intensifies ecosystem disturbance relative to prior land use. We compared sites left in historical land uses of either agriculture (AG) or Conservation Reserve Program grassland (CRP) to those that were converted to corn or switchgrass bioenergy crop production. Enzyme activities, mass loss, microbial biomass, and changes in litter chemistry were monitored in corn stover and switchgrass litter over 485 days, accompanied by similar soil measurements. Across all measured variables, legacy had the strongest effect (P < 0.05) relative to litter type and current management, where CRP sites maintained higher soil and litter enzyme activities and microbial biomass relative to AG sites. Decomposition responses to conversion depended on legacy but also current management and litter type. Within the CRP sites, conversion into corn increased litter enzymes, microbial biomass, and litter protein and lipid abundances, especially on decomposing corn litter, relative to nonconverted CRP. However, conversion into switchgrass from CRP, a moderate disturbance, often had no effect on switchgrass litter decomposition parameters. Thus, legacies shape the direction and magnitude of decomposition responses to bioenergy crop conversion and therefore should be considered a key influence on litter and soil C cycling under bioenergy crop management.« less

Heterogeneous decomposition of silane in a fixed bed reactor

NASA Technical Reports Server (NTRS)

Iya, S. K.; Flagella, R. N.; Dipaolo, F. S.

1982-01-01

Heterogeneous decomposition of silane in a fluidized bed offers an attractive route for the low-cost production of silicon for photovoltaic application. To obtain design data for a fluid bed silane pyrolysis reactor, deposition experiments were conducted in a small-scale fixed bed apparatus. Data on the decomposition mode, plating rate, and deposition morphology were obtained in the temperature range 600-900 C. Conditions favorable for heterogeneous decomposition with good deposition morphology were identified. The kinetic rate data showed the reaction to be first order with an activation energy of 38.8 kcal/mol, which agrees well with work done by others. The results are promising for the development of an economically attractive fluid bed process.

Fine root dynamics across a chronosequence of upland temperate deciduous forests

Treesearch

Travis W. Idol; Phillip E. Pope; Felix Jr. Ponder

2000-01-01

Following a major disturbance event in forests that removes most of the standing vegetation, patterns of fine root growth, mortality, and decomposition may be altered from the pre-disturbance conditions. The objective of this study was to describe the changes in the seasonal and spatial dynamics of fine root growth, mortality, and decomposition that occur following...

Low-frequency Raman scattering in a Xe hydrate.

PubMed

Adichtchev, S V; Belosludov, V R; Ildyakov, A V; Malinovsky, V K; Manakov, A Yu; Subbotin, O S; Surovtsev, N V

2013-09-12

The physics of gas hydrates are rich in interesting phenomena such as anomalies for thermal conductivity, self-preservation effects for decomposition, and others. Some of these phenomena are presumably attributed to the resonance interaction of the rattling motions of guest molecules or atoms with the lattice modes. This can be expected to induce some specific features in the low-frequency (THz) vibrational response. Here we present results for low-frequency Raman scattering in a Xe hydrate, supported by numerical calculations of vibrational density of states. A number of narrow lines, located in the range from 18 to 90 cm(-1), were found in the Raman spectrum. Numerical calculations confirm that these lines correspond to resonance modes of the Xe hydrate. Also, low-frequency Raman scattering was studied during gas hydrate decomposition, and two scenarios were observed. The first one is the direct decomposition of the Xe hydrate to water and gas. The second one is the hydrate decomposition to ice and gas with subsequent melting of ice. In the latter case, a transient low-frequency Raman band is observed, which is associated with low-frequency bands (e.g., boson peak) of disordered solids.

Complete ensemble local mean decomposition with adaptive noise and its application to fault diagnosis for rolling bearings

NASA Astrophysics Data System (ADS)

Wang, Lei; Liu, Zhiwen; Miao, Qiang; Zhang, Xin

2018-06-01

Mode mixing resulting from intermittent signals is an annoying problem associated with the local mean decomposition (LMD) method. Based on noise-assisted approach, ensemble local mean decomposition (ELMD) method alleviates the mode mixing issue of LMD to some degree. However, the product functions (PFs) produced by ELMD often contain considerable residual noise, and thus a relatively large number of ensemble trials are required to eliminate the residual noise. Furthermore, since different realizations of Gaussian white noise are added to the original signal, different trials may generate different number of PFs, making it difficult to take ensemble mean. In this paper, a novel method is proposed called complete ensemble local mean decomposition with adaptive noise (CELMDAN) to solve these two problems. The method adds a particular and adaptive noise at every decomposition stage for each trial. Moreover, a unique residue is obtained after separating each PF, and the obtained residue is used as input for the next stage. Two simulated signals are analyzed to illustrate the advantages of CELMDAN in comparison to ELMD and CEEMDAN. To further demonstrate the efficiency of CELMDAN, the method is applied to diagnose faults for rolling bearings in an experimental case and an engineering case. The diagnosis results indicate that CELMDAN can extract more fault characteristic information with less interference than ELMD.

Fractal Dynamics of Heartbeat Interval Fluctuations in Health and Disease

NASA Astrophysics Data System (ADS)

Meyer, M.; Marconi, C.; Rahmel, A.; Grassi, B.; Ferretti, G.; Skinner, J. E.; Cerretelli, P.

The dynamics of heartbeat interval time series were studied by a modified random walk analysis recently introduced as Detrended Fluctuation Analysis. In this analysis, the intrinsic fractal long-range power-law correlation properties of beat-to-beat fluctuations generated by the dynamical system (i.e. cardiac rhythm generator), after decomposition from extrinsic uncorrelated sources, can be quantified by the scaling exponent which, in healthy subjects, is about 1.0. The finding of a scaling coefficient of 1.0, indicating scale-invariant long-range power-law correlations (1/ƒnoise) of heartbeat fluctuations, would reflect a genuinely self-similar fractal process that typically generates fluctuations on a wide range of time scales. Lack of a characteristic time scale suggests that the neuroautonomic system underlying the control of heart rate dynamics helps prevent excessive mode-locking (error tolerance) that would restrict its functional responsiveness (plasticity) to environmental stimuli. The 1/ƒ dynamics of heartbeat interval fluctuations are unaffected by exposure to chronic hypoxia suggesting that the neuroautonomic cardiac control system is preadapted to hypoxia. Functional (hypothermia, cardiac disease) and/or structural (cardiac transplantation, early cardiac development) inactivation of neuroautonomic control is associated with the breakdown or absence of fractal complexity reflected by anticorrelated random walk-like dynamics, indicating that in these conditions the heart is unadapted to its environment.

Molecular hydrodynamics: Vortex formation and sound wave propagation

DOE PAGES

Han, Kyeong Hwan; Kim, Changho; Talkner, Peter; ...

2018-01-14

In the present study, quantitative feasibility tests of the hydrodynamic description of a two-dimensional fluid at the molecular level are performed, both with respect to length and time scales. Using high-resolution fluid velocity data obtained from extensive molecular dynamics simulations, we computed the transverse and longitudinal components of the velocity field by the Helmholtz decomposition and compared them with those obtained from the linearized Navier-Stokes (LNS) equations with time-dependent transport coefficients. By investigating the vortex dynamics and the sound wave propagation in terms of these field components, we confirm the validity of the LNS description for times comparable to ormore » larger than several mean collision times. The LNS description still reproduces the transverse velocity field accurately at smaller times, but it fails to predict characteristic patterns of molecular origin visible in the longitudinal velocity field. Based on these observations, we validate the main assumptions of the mode-coupling approach. The assumption that the velocity autocorrelation function can be expressed in terms of the fluid velocity field and the tagged particle distribution is found to be remarkably accurate even for times comparable to or smaller than the mean collision time. This suggests that the hydrodynamic-mode description remains valid down to the molecular scale.« less

Flexible Launch Vehicle Stability Analysis Using Steady and Unsteady Computational Fluid Dynamics

NASA Technical Reports Server (NTRS)

Bartels, Robert E.

2012-01-01

Launch vehicles frequently experience a reduced stability margin through the transonic Mach number range. This reduced stability margin can be caused by the aerodynamic undamping one of the lower-frequency flexible or rigid body modes. Analysis of the behavior of a flexible vehicle is routinely performed with quasi-steady aerodynamic line loads derived from steady rigid aerodynamics. However, a quasi-steady aeroelastic stability analysis can be unconservative at the critical Mach numbers, where experiment or unsteady computational aeroelastic analysis show a reduced or even negative aerodynamic damping.Amethod of enhancing the quasi-steady aeroelastic stability analysis of a launch vehicle with unsteady aerodynamics is developed that uses unsteady computational fluid dynamics to compute the response of selected lower-frequency modes. The response is contained in a time history of the vehicle line loads. A proper orthogonal decomposition of the unsteady aerodynamic line-load response is used to reduce the scale of data volume and system identification is used to derive the aerodynamic stiffness, damping, and mass matrices. The results are compared with the damping and frequency computed from unsteady computational aeroelasticity and from a quasi-steady analysis. The results show that incorporating unsteady aerodynamics in this way brings the enhanced quasi-steady aeroelastic stability analysis into close agreement with the unsteady computational aeroelastic results.

Direct recovery of regional tracer kinetics from temporally inconsistent dynamic ECT projections using dimension-reduced time-activity basis

NASA Astrophysics Data System (ADS)

Maltz, Jonathan S.

2000-11-01

We present an algorithm of reduced computational cost which is able to estimate kinetic model parameters directly from dynamic ECT sinograms made up of temporally inconsistent projections. The algorithm exploits the extreme degree of parameter redundancy inherent in linear combinations of the exponential functions which represent the modes of first-order compartmental systems. The singular value decomposition is employed to find a small set of orthogonal functions, the linear combinations of which are able to accurately represent all modes within the physiologically anticipated range in a given study. The reduced-dimension basis is formed as the convolution of this orthogonal set with a measured input function. The Moore-Penrose pseudoinverse is used to find coefficients of this basis. Algorithm performance is evaluated at realistic count rates using MCAT phantom and clinical 99mTc-teboroxime myocardial study data. Phantom data are modelled as originating from a Poisson process. For estimates recovered from a single slice projection set containing 2.5×105 total counts, recovered tissue responses compare favourably with those obtained using more computationally intensive methods. The corresponding kinetic parameter estimates (coefficients of the new basis) exhibit negligible bias, while parameter variances are low, falling within 30% of the Cramér-Rao lower bound.

Molecular hydrodynamics: Vortex formation and sound wave propagation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Han, Kyeong Hwan; Kim, Changho; Talkner, Peter

In the present study, quantitative feasibility tests of the hydrodynamic description of a two-dimensional fluid at the molecular level are performed, both with respect to length and time scales. Using high-resolution fluid velocity data obtained from extensive molecular dynamics simulations, we computed the transverse and longitudinal components of the velocity field by the Helmholtz decomposition and compared them with those obtained from the linearized Navier-Stokes (LNS) equations with time-dependent transport coefficients. By investigating the vortex dynamics and the sound wave propagation in terms of these field components, we confirm the validity of the LNS description for times comparable to ormore » larger than several mean collision times. The LNS description still reproduces the transverse velocity field accurately at smaller times, but it fails to predict characteristic patterns of molecular origin visible in the longitudinal velocity field. Based on these observations, we validate the main assumptions of the mode-coupling approach. The assumption that the velocity autocorrelation function can be expressed in terms of the fluid velocity field and the tagged particle distribution is found to be remarkably accurate even for times comparable to or smaller than the mean collision time. This suggests that the hydrodynamic-mode description remains valid down to the molecular scale.« less

Differences in the sensitivity of fungi and bacteria to season and invertebrates affect leaf litter decomposition in a Mediterranean stream.

PubMed

Mora-Gómez, Juanita; Elosegi, Arturo; Duarte, Sofia; Cássio, Fernanda; Pascoal, Cláudia; Romaní, Anna M

2016-08-01

Microorganisms are key drivers of leaf litter decomposition; however, the mechanisms underlying the dynamics of different microbial groups are poorly understood. We investigated the effects of seasonal variation and invertebrates on fungal and bacterial dynamics, and on leaf litter decomposition. We followed the decomposition of Populus nigra litter in a Mediterranean stream through an annual cycle, using fine and coarse mesh bags. Irrespective of the season, microbial decomposition followed two stages. Initially, bacterial contribution to total microbial biomass was higher compared to later stages, and it was related to disaccharide and lignin degradation; in a later stage, bacteria were less important and were associated with hemicellulose and cellulose degradation, while fungi were related to lignin decomposition. The relevance of microbial groups in decomposition differed among seasons: fungi were more important in spring, whereas in summer, water quality changes seemed to favour bacteria and slowed down lignin and hemicellulose degradation. Invertebrates influenced litter-associated microbial assemblages (especially bacteria), stimulated enzyme efficiencies and reduced fungal biomass. We conclude that bacterial and fungal assemblages play distinctive roles in microbial decomposition and differ in their sensitivity to environmental changes, ultimately affecting litter decomposition, which might be particularly relevant in highly seasonal ecosystems, such as intermittent streams. © FEMS 2016. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Modal analysis of the thermal conductivity of nanowires: examining unique thermal transport features.

PubMed

Samaraweera, Nalaka; Larkin, Jason M; Chan, Kin L; Mithraratne, Kumar

2018-06-06

In this study, unique thermal transport features of nanowires over bulk materials are investigated using a combined analysis based on lattice dynamics and equilibrium molecular dynamics (EMD). The evaluation of the thermal conductivity (TC) of Lenard-Jones nanowires becomes feasible due to the multi-step normal mode decomposition (NMD) procedure implemented in the study. A convergence issue of the TC of nanowires is addressed by the NMD implementation for two case studies, which employ pristine nanowires (PNW) and superlattice nanowires. Interestingly, mode relaxation times at low frequencies of acoustic branches exhibit signs of approaching constant values, thus indicating the convergence of TC. The TC evaluation procedure is further verified by implementing EMD-based Green-Kubo analysis, which is based on a fundamentally different physical perspective. Having verified the NMD procedure, the non-monotonic trend of the TC of nanowires is addressed. It is shown that the principal cause for the observed trend is due to the competing effects of long wavelength phonons and phonon-surface scatterings as the nanowire's cross-sectional width is changed. A computational procedure is developed to decompose the different modal contribution to the TC of shell alloy nanowires (SANWs) using virtual crystal NMD and the Allen-Feldman theory. Several important conclusions can be drawn from the results. A propagons to non-propagons boundary appeared, resulting in a cut-off frequency (ω cut ); moreover, as alloy atomic mass is increased, ω cut shifts to lower frequencies. The existence of non-propagons partly causes the low TC of SANWs. It can be seen that modes with low frequencies demonstrate a similar behavior to corresponding modes of PNWs. Moreover, lower group velocities associated with higher alloy atomic mass resulted in a lower TC of SANWs.

Modal analysis of the thermal conductivity of nanowires: examining unique thermal transport features

NASA Astrophysics Data System (ADS)

Samaraweera, Nalaka; Larkin, Jason M.; Chan, Kin L.; Mithraratne, Kumar

2018-06-01

In this study, unique thermal transport features of nanowires over bulk materials are investigated using a combined analysis based on lattice dynamics and equilibrium molecular dynamics (EMD). The evaluation of the thermal conductivity (TC) of Lenard–Jones nanowires becomes feasible due to the multi-step normal mode decomposition (NMD) procedure implemented in the study. A convergence issue of the TC of nanowires is addressed by the NMD implementation for two case studies, which employ pristine nanowires (PNW) and superlattice nanowires. Interestingly, mode relaxation times at low frequencies of acoustic branches exhibit signs of approaching constant values, thus indicating the convergence of TC. The TC evaluation procedure is further verified by implementing EMD-based Green–Kubo analysis, which is based on a fundamentally different physical perspective. Having verified the NMD procedure, the non-monotonic trend of the TC of nanowires is addressed. It is shown that the principal cause for the observed trend is due to the competing effects of long wavelength phonons and phonon–surface scatterings as the nanowire’s cross-sectional width is changed. A computational procedure is developed to decompose the different modal contribution to the TC of shell alloy nanowires (SANWs) using virtual crystal NMD and the Allen–Feldman theory. Several important conclusions can be drawn from the results. A propagons to non-propagons boundary appeared, resulting in a cut-off frequency (ω cut); moreover, as alloy atomic mass is increased, ω cut shifts to lower frequencies. The existence of non-propagons partly causes the low TC of SANWs. It can be seen that modes with low frequencies demonstrate a similar behavior to corresponding modes of PNWs. Moreover, lower group velocities associated with higher alloy atomic mass resulted in a lower TC of SANWs.

Nonequilibrium adiabatic molecular dynamics simulations of methane clathrate hydrate decomposition

NASA Astrophysics Data System (ADS)

Alavi, Saman; Ripmeester, J. A.

2010-04-01

Nonequilibrium, constant energy, constant volume (NVE) molecular dynamics simulations are used to study the decomposition of methane clathrate hydrate in contact with water. Under adiabatic conditions, the rate of methane clathrate decomposition is affected by heat and mass transfer arising from the breakup of the clathrate hydrate framework and release of the methane gas at the solid-liquid interface and diffusion of methane through water. We observe that temperature gradients are established between the clathrate and solution phases as a result of the endothermic clathrate decomposition process and this factor must be considered when modeling the decomposition process. Additionally we observe that clathrate decomposition does not occur gradually with breakup of individual cages, but rather in a concerted fashion with rows of structure I cages parallel to the interface decomposing simultaneously. Due to the concerted breakup of layers of the hydrate, large amounts of methane gas are released near the surface which can form bubbles that will greatly affect the rate of mass transfer near the surface of the clathrate phase. The effects of these phenomena on the rate of methane hydrate decomposition are determined and implications on hydrate dissociation in natural methane hydrate reservoirs are discussed.

Nonequilibrium adiabatic molecular dynamics simulations of methane clathrate hydrate decomposition.

PubMed

Alavi, Saman; Ripmeester, J A

2010-04-14

Nonequilibrium, constant energy, constant volume (NVE) molecular dynamics simulations are used to study the decomposition of methane clathrate hydrate in contact with water. Under adiabatic conditions, the rate of methane clathrate decomposition is affected by heat and mass transfer arising from the breakup of the clathrate hydrate framework and release of the methane gas at the solid-liquid interface and diffusion of methane through water. We observe that temperature gradients are established between the clathrate and solution phases as a result of the endothermic clathrate decomposition process and this factor must be considered when modeling the decomposition process. Additionally we observe that clathrate decomposition does not occur gradually with breakup of individual cages, but rather in a concerted fashion with rows of structure I cages parallel to the interface decomposing simultaneously. Due to the concerted breakup of layers of the hydrate, large amounts of methane gas are released near the surface which can form bubbles that will greatly affect the rate of mass transfer near the surface of the clathrate phase. The effects of these phenomena on the rate of methane hydrate decomposition are determined and implications on hydrate dissociation in natural methane hydrate reservoirs are discussed.

Time-dependent density functional theory for open systems with a positivity-preserving decomposition scheme for environment spectral functions

NASA Astrophysics Data System (ADS)

Wang, RuLin; Zheng, Xiao; Kwok, YanHo; Xie, Hang; Chen, GuanHua; Yam, ChiYung

2015-04-01

Understanding electronic dynamics on material surfaces is fundamentally important for applications including nanoelectronics, inhomogeneous catalysis, and photovoltaics. Practical approaches based on time-dependent density functional theory for open systems have been developed to characterize the dissipative dynamics of electrons in bulk materials. The accuracy and reliability of such approaches depend critically on how the electronic structure and memory effects of surrounding material environment are accounted for. In this work, we develop a novel squared-Lorentzian decomposition scheme, which preserves the positive semi-definiteness of the environment spectral matrix. The resulting electronic dynamics is guaranteed to be both accurate and convergent even in the long-time limit. The long-time stability of electronic dynamics simulation is thus greatly improved within the current decomposition scheme. The validity and usefulness of our new approach are exemplified via two prototypical model systems: quasi-one-dimensional atomic chains and two-dimensional bilayer graphene.

Thermal Decomposition of the Solid Phase of Nitromethane: Ab Initio Molecular Dynamics Simulations

NASA Astrophysics Data System (ADS)

Chang, Jing; Lian, Peng; Wei, Dong-Qing; Chen, Xiang-Rong; Zhang, Qing-Ming; Gong, Zi-Zheng

2010-10-01

The Car-Parrinello molecular dynamics simulations were employed to investigate thermal decomposition of the solid nitromethane. It is found that it undergoes chemical decomposition at about 2200 K under ambient pressure. The initiation of reactions involves both proton transfer and commonly known C-N bond cleavage. About 75 species and 100 elementary reactions were observed with the final products being H2O, CO2, N2, and CNCNC. It represents the first complete simulation of solid-phase explosive reactions reported to date, which is of far-reaching implication for design and development of new energetic materials.

Local dynamics and spatiotemporal chaos. The Kuramoto- Sivashinsky equation: A case study

NASA Astrophysics Data System (ADS)

Wittenberg, Ralf Werner

The nature of spatiotemporal chaos in extended continuous systems is not yet well-understood. In this thesis, a model partial differential equation, the Kuramoto- Sivashinsky (KS) equation ut+uxxxx+uxx+uux =0 on a large one-dimensional periodic domain, is studied analytically, numerically, and through modeling to obtain a more detailed understanding of the observed spatiotemporally complex dynamics. In particular, with the aid of a wavelet decomposition, the relevant dynamical interactions are shown to be localized in space and scale. Motivated by these results, and by the idea that the attractor on a large domain may be understood via attractors on smaller domains, a spatially localized low- dimensional model for a minimal chaotic box is proposed. A (de)stabilized extension of the KS equation has recently attracted increased interest; for this situation, dissipativity and analyticity areproven, and an explicit shock-like solution is constructed which sheds light on the difficulties in obtaining optimal bounds for the KS equation. For the usual KS equation, the spatiotemporally chaotic state is carefully characterized in real, Fourier and wavelet space. The wavelet decomposition provides good scale separation which isolates the three characteristic regions of the dynamics: large scales of slow Gaussian fluctuations, active scales containing localized interactions of coherent structures, and small scales. Space localization is shown through a comparison of various correlation lengths and a numerical experiment in which different modes are uncoupled to estimate a dynamic interaction length. A detailed picture of the contributions of different scales to the spatiotemporally complex dynamics is obtained via a Galerkin projection of the KS equation onto the wavelet basis, and an extensive series of numerical experiments in which different combinations of wavelet levels are eliminated or forced. These results, and a formalism to derive an effective equation for periodized subsystems externally forced from a larger system, motivate various models for spatially localized forced systems. There is convincing evidence that short periodized systems, internally forced at the largest scales, form a minimal model for the observed extensively chaotic dynamics in larger domains.

Measure of the electroencephalographic effects of sevoflurane using recurrence dynamics.

PubMed

Li, Xiaoli; Sleigh, Jamie W; Voss, Logan J; Ouyang, Gaoxiang

2007-08-31

This paper proposes a novel method to interpret the effect of anesthetic agents (sevoflurane) on the neural activity, by using recurrence quantification analysis of EEG data. First, we reduce the artefacts in the scalp EEG using a novel filter that combines wavelet transforms and empirical mode decomposition. Then, the determinism in the recurrence plot is calculated. It is found that the determinism increases gradually with increasing the concentration of sevoflurane. Finally, a pharmacokinetic and pharmacodynamic (PKPD) model is built to describe the relationship between the concentration of sevoflurane and the processed EEG measure ('determinism' of the recurrence plot). A test sample of nine patients shows the recurrence in EEG data may track the effect of the sevoflurane on the brain.

Ensemble empirical mode decomposition based fluorescence spectral noise reduction for low concentration PAHs

NASA Astrophysics Data System (ADS)

Wang, Shu-tao; Yang, Xue-ying; Kong, De-ming; Wang, Yu-tian

2017-11-01

A new noise reduction method based on ensemble empirical mode decomposition (EEMD) is proposed to improve the detection effect for fluorescence spectra. Polycyclic aromatic hydrocarbons (PAHs) pollutants, as a kind of important current environmental pollution source, are highly oncogenic. Using the fluorescence spectroscopy method, the PAHs pollutants can be detected. However, instrument will produce noise in the experiment. Weak fluorescent signals can be affected by noise, so we propose a way to denoise and improve the detection effect. Firstly, we use fluorescence spectrometer to detect PAHs to obtain fluorescence spectra. Subsequently, noises are reduced by EEMD algorithm. Finally, the experiment results show the proposed method is feasible.

Multi-faults decoupling on turbo-expander using differential-based ensemble empirical mode decomposition

NASA Astrophysics Data System (ADS)

Li, Hongguang; Li, Ming; Li, Cheng; Li, Fucai; Meng, Guang

2017-09-01

This paper dedicates on the multi-faults decoupling of turbo-expander rotor system using Differential-based Ensemble Empirical Mode Decomposition (DEEMD). DEEMD is an improved version of DEMD to resolve the imperfection of mode mixing. The nonlinear behaviors of the turbo-expander considering temperature gradient with crack, rub-impact and pedestal looseness faults are investigated respectively, so that the baseline for the multi-faults decoupling can be established. DEEMD is then utilized on the vibration signals of the rotor system with coupling faults acquired by numerical simulation, and the results indicate that DEEMD can successfully decouple the coupling faults, which is more efficient than EEMD. DEEMD is also applied on the vibration signal of the misalignment coupling with rub-impact fault obtained during the adjustment of the experimental system. The conclusion shows that DEEMD can decompose the practical multi-faults signal and the industrial prospect of DEEMD is verified as well.

Decomposition Technique for Remaining Useful Life Prediction

NASA Technical Reports Server (NTRS)

Saha, Bhaskar (Inventor); Goebel, Kai F. (Inventor); Saxena, Abhinav (Inventor); Celaya, Jose R. (Inventor)

2014-01-01

The prognostic tool disclosed here decomposes the problem of estimating the remaining useful life (RUL) of a component or sub-system into two separate regression problems: the feature-to-damage mapping and the operational conditions-to-damage-rate mapping. These maps are initially generated in off-line mode. One or more regression algorithms are used to generate each of these maps from measurements (and features derived from these), operational conditions, and ground truth information. This decomposition technique allows for the explicit quantification and management of different sources of uncertainty present in the process. Next, the maps are used in an on-line mode where run-time data (sensor measurements and operational conditions) are used in conjunction with the maps generated in off-line mode to estimate both current damage state as well as future damage accumulation. Remaining life is computed by subtracting the instance when the extrapolated damage reaches the failure threshold from the instance when the prediction is made.

Tourism forecasting using modified empirical mode decomposition and group method of data handling

NASA Astrophysics Data System (ADS)

Yahya, N. A.; Samsudin, R.; Shabri, A.

2017-09-01

In this study, a hybrid model using modified Empirical Mode Decomposition (EMD) and Group Method of Data Handling (GMDH) model is proposed for tourism forecasting. This approach reconstructs intrinsic mode functions (IMFs) produced by EMD using trial and error method. The new component and the remaining IMFs is then predicted respectively using GMDH model. Finally, the forecasted results for each component are aggregated to construct an ensemble forecast. The data used in this experiment are monthly time series data of tourist arrivals from China, Thailand and India to Malaysia from year 2000 to 2016. The performance of the model is evaluated using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) where conventional GMDH model and EMD-GMDH model are used as benchmark models. Empirical results proved that the proposed model performed better forecasts than the benchmarked models.

An epileptic seizures detection algorithm based on the empirical mode decomposition of EEG.

PubMed

Orosco, Lorena; Laciar, Eric; Correa, Agustina Garces; Torres, Abel; Graffigna, Juan P

2009-01-01

Epilepsy is a neurological disorder that affects around 50 million people worldwide. The seizure detection is an important component in the diagnosis of epilepsy. In this study, the Empirical Mode Decomposition (EMD) method was proposed on the development of an automatic epileptic seizure detection algorithm. The algorithm first computes the Intrinsic Mode Functions (IMFs) of EEG records, then calculates the energy of each IMF and performs the detection based on an energy threshold and a minimum duration decision. The algorithm was tested in 9 invasive EEG records provided and validated by the Epilepsy Center of the University Hospital of Freiburg. In 90 segments analyzed (39 with epileptic seizures) the sensitivity and specificity obtained with the method were of 56.41% and 75.86% respectively. It could be concluded that EMD is a promissory method for epileptic seizure detection in EEG records.

Fringe-projection profilometry based on two-dimensional empirical mode decomposition.

PubMed

Zheng, Suzhen; Cao, Yiping

2013-11-01

In 3D shape measurement, because deformed fringes often contain low-frequency information degraded with random noise and background intensity information, a new fringe-projection profilometry is proposed based on 2D empirical mode decomposition (2D-EMD). The fringe pattern is first decomposed into numbers of intrinsic mode functions by 2D-EMD. Because the method has partial noise reduction, the background components can be removed to obtain the fundamental components needed to perform Hilbert transformation to retrieve the phase information. The 2D-EMD can effectively extract the modulation phase of a single direction fringe and an inclined fringe pattern because it is a full 2D analysis method and considers the relationship between adjacent lines of a fringe patterns. In addition, as the method does not add noise repeatedly, as does ensemble EMD, the data processing time is shortened. Computer simulations and experiments prove the feasibility of this method.

Heterogeneous Tensor Decomposition for Clustering via Manifold Optimization.

PubMed

Sun, Yanfeng; Gao, Junbin; Hong, Xia; Mishra, Bamdev; Yin, Baocai

2016-03-01

Tensor clustering is an important tool that exploits intrinsically rich structures in real-world multiarray or Tensor datasets. Often in dealing with those datasets, standard practice is to use subspace clustering that is based on vectorizing multiarray data. However, vectorization of tensorial data does not exploit complete structure information. In this paper, we propose a subspace clustering algorithm without adopting any vectorization process. Our approach is based on a novel heterogeneous Tucker decomposition model taking into account cluster membership information. We propose a new clustering algorithm that alternates between different modes of the proposed heterogeneous tensor model. All but the last mode have closed-form updates. Updating the last mode reduces to optimizing over the multinomial manifold for which we investigate second order Riemannian geometry and propose a trust-region algorithm. Numerical experiments show that our proposed algorithm compete effectively with state-of-the-art clustering algorithms that are based on tensor factorization.

Analysis of Vibration and Noise of Construction Machinery Based on Ensemble Empirical Mode Decomposition and Spectral Correlation Analysis Method

NASA Astrophysics Data System (ADS)

Chen, Yuebiao; Zhou, Yiqi; Yu, Gang; Lu, Dan

In order to analyze the effect of engine vibration on cab noise of construction machinery in multi-frequency bands, a new method based on ensemble empirical mode decomposition (EEMD) and spectral correlation analysis is proposed. Firstly, the intrinsic mode functions (IMFs) of vibration and noise signals were obtained by EEMD method, and then the IMFs which have the same frequency bands were selected. Secondly, we calculated the spectral correlation coefficients between the selected IMFs, getting the main frequency bands in which engine vibration has significant impact on cab noise. Thirdly, the dominated frequencies were picked out and analyzed by spectral analysis method. The study result shows that the main frequency bands and dominated frequencies in which engine vibration have serious impact on cab noise can be identified effectively by the proposed method, which provides effective guidance to noise reduction of construction machinery.

Telephone-quality pathological speech classification using empirical mode decomposition.

PubMed

Kaleem, M F; Ghoraani, B; Guergachi, A; Krishnan, S

2011-01-01

This paper presents a computationally simple and effective methodology based on empirical mode decomposition (EMD) for classification of telephone quality normal and pathological speech signals. EMD is used to decompose continuous normal and pathological speech signals into intrinsic mode functions, which are analyzed to extract physically meaningful and unique temporal and spectral features. Using continuous speech samples from a database of 51 normal and 161 pathological speakers, which has been modified to simulate telephone quality speech under different levels of noise, a linear classifier is used with the feature vector thus obtained to obtain a high classification accuracy, thereby demonstrating the effectiveness of the methodology. The classification accuracy reported in this paper (89.7% for signal-to-noise ratio 30 dB) is a significant improvement over previously reported results for the same task, and demonstrates the utility of our methodology for cost-effective remote voice pathology assessment over telephone channels.

Forecasting stochastic neural network based on financial empirical mode decomposition.

PubMed

Wang, Jie; Wang, Jun

2017-06-01

In an attempt to improve the forecasting accuracy of stock price fluctuations, a new one-step-ahead model is developed in this paper which combines empirical mode decomposition (EMD) with stochastic time strength neural network (STNN). The EMD is a processing technique introduced to extract all the oscillatory modes embedded in a series, and the STNN model is established for considering the weight of occurrence time of the historical data. The linear regression performs the predictive availability of the proposed model, and the effectiveness of EMD-STNN is revealed clearly through comparing the predicted results with the traditional models. Moreover, a new evaluated method (q-order multiscale complexity invariant distance) is applied to measure the predicted results of real stock index series, and the empirical results show that the proposed model indeed displays a good performance in forecasting stock market fluctuations. Copyright © 2017 Elsevier Ltd. All rights reserved.

MASS LOSS AND NITROGEN DYNAMICS DURING THE DECOMPOSITION OF A N-LABELED N2-FIXING EPOPHYTIC LICHEN, LOBARIA OREGANA (TUCK.) MULL. ARG.

EPA Science Inventory

We studied mass loss and nitrogen dynamics during fall and spring initiated decomposition of an N2-fixing epiphytic lichen, Lobaria oregana (Tuck.) Mull. Arg. using 15N. We developed a method of labeling lichens with 15N that involved spraying lichen material with a nutrient sol...

Exploration of laser-driven electron-multirescattering dynamics in high-order harmonic generation

DOE PAGES

Li, Peng -Cheng; Sheu, Yae -Lin; Jooya, Hossein Z.; ...

2016-09-06

Multiple rescattering processes play an important role in high-order harmonic generation (HHG) in an intense laser field. However, the underlying multi-rescattering dynamics are still largely unexplored. Here we investigate the dynamical origin of multiple rescattering processes in HHG associated with the odd and even number of returning times of the electron to the parent ion. We perform fully ab initio quantum calculations and extend the empirical mode decomposition method to extract the individual multiple scattering contributions in HHG. We find that the tunneling ionization regime is responsible for the odd number times of rescattering and the corresponding short trajectories aremore » dominant. On the other hand, the multiphoton ionization regime is responsible for the even number times of rescattering and the corresponding long trajectories are dominant. Moreover, we discover that the multiphoton- and tunneling-ionization regimes in multiple rescattering processes occur alternatively. Our results uncover the dynamical origin of multiple rescattering processes in HHG for the first time. As a result, it also provides new insight regarding the control of the multiple rescattering processes for the optimal generation of ultrabroad band supercontinuum spectra and the production of single ultrashort attosecond laser pulse.« less

Exploration of laser-driven electron-multirescattering dynamics in high-order harmonic generation

PubMed Central

Li, Peng-Cheng; Sheu, Yae-Lin; Jooya, Hossein Z.; Zhou, Xiao-Xin; Chu, Shih-I

2016-01-01

Multiple rescattering processes play an important role in high-order harmonic generation (HHG) in an intense laser field. However, the underlying multi-rescattering dynamics are still largely unexplored. Here we investigate the dynamical origin of multiple rescattering processes in HHG associated with the odd and even number of returning times of the electron to the parent ion. We perform fully ab initio quantum calculations and extend the empirical mode decomposition method to extract the individual multiple scattering contributions in HHG. We find that the tunneling ionization regime is responsible for the odd number times of rescattering and the corresponding short trajectories are dominant. On the other hand, the multiphoton ionization regime is responsible for the even number times of rescattering and the corresponding long trajectories are dominant. Moreover, we discover that the multiphoton- and tunneling-ionization regimes in multiple rescattering processes occur alternatively. Our results uncover the dynamical origin of multiple rescattering processes in HHG for the first time. It also provides new insight regarding the control of the multiple rescattering processes for the optimal generation of ultrabroad band supercontinuum spectra and the production of single ultrashort attosecond laser pulse. PMID:27596056

Exploration of laser-driven electron-multirescattering dynamics in high-order harmonic generation.

PubMed

Li, Peng-Cheng; Sheu, Yae-Lin; Jooya, Hossein Z; Zhou, Xiao-Xin; Chu, Shih-I

2016-09-06

Multiple rescattering processes play an important role in high-order harmonic generation (HHG) in an intense laser field. However, the underlying multi-rescattering dynamics are still largely unexplored. Here we investigate the dynamical origin of multiple rescattering processes in HHG associated with the odd and even number of returning times of the electron to the parent ion. We perform fully ab initio quantum calculations and extend the empirical mode decomposition method to extract the individual multiple scattering contributions in HHG. We find that the tunneling ionization regime is responsible for the odd number times of rescattering and the corresponding short trajectories are dominant. On the other hand, the multiphoton ionization regime is responsible for the even number times of rescattering and the corresponding long trajectories are dominant. Moreover, we discover that the multiphoton- and tunneling-ionization regimes in multiple rescattering processes occur alternatively. Our results uncover the dynamical origin of multiple rescattering processes in HHG for the first time. It also provides new insight regarding the control of the multiple rescattering processes for the optimal generation of ultrabroad band supercontinuum spectra and the production of single ultrashort attosecond laser pulse.

Dynamic load balancing algorithm for molecular dynamics based on Voronoi cells domain decompositions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fattebert, J.-L.; Richards, D.F.; Glosli, J.N.

2012-12-01

We present a new algorithm for automatic parallel load balancing in classical molecular dynamics. It assumes a spatial domain decomposition of particles into Voronoi cells. It is a gradient method which attempts to minimize a cost function by displacing Voronoi sites associated with each processor/sub-domain along steepest descent directions. Excellent load balance has been obtained for quasi-2D and 3D practical applications, with up to 440·10 6 particles on 65,536 MPI tasks.

Empirical Mode Decomposition and k-Nearest Embedding Vectors for Timely Analyses of Antibiotic Resistance Trends

PubMed Central

Teodoro, Douglas; Lovis, Christian

2013-01-01

Background Antibiotic resistance is a major worldwide public health concern. In clinical settings, timely antibiotic resistance information is key for care providers as it allows appropriate targeted treatment or improved empirical treatment when the specific results of the patient are not yet available. Objective To improve antibiotic resistance trend analysis algorithms by building a novel, fully data-driven forecasting method from the combination of trend extraction and machine learning models for enhanced biosurveillance systems. Methods We investigate a robust model for extraction and forecasting of antibiotic resistance trends using a decade of microbiology data. Our method consists of breaking down the resistance time series into independent oscillatory components via the empirical mode decomposition technique. The resulting waveforms describing intrinsic resistance trends serve as the input for the forecasting algorithm. The algorithm applies the delay coordinate embedding theorem together with the k-nearest neighbor framework to project mappings from past events into the future dimension and estimate the resistance levels. Results The algorithms that decompose the resistance time series and filter out high frequency components showed statistically significant performance improvements in comparison with a benchmark random walk model. We present further qualitative use-cases of antibiotic resistance trend extraction, where empirical mode decomposition was applied to highlight the specificities of the resistance trends. Conclusion The decomposition of the raw signal was found not only to yield valuable insight into the resistance evolution, but also to produce novel models of resistance forecasters with boosted prediction performance, which could be utilized as a complementary method in the analysis of antibiotic resistance trends. PMID:23637796

Dynamic mode decomposition of separated flow over a finite blunt plate: time-resolved particle image velocimetry measurements

NASA Astrophysics Data System (ADS)

Liu, Yingzheng; Zhang, Qingshan

2015-07-01

Dynamic mode decomposition (DMD) analysis was performed on a large number of realizations of the separated flow around a finite blunt plate, which were determined by using planar time-resolved particle image velocimetry (TR-PIV). Three plates with different chord-to-thickness ratios corresponding to globally different flow patterns were particularly selected for comparison: L/D = 3.0, 6.0 and 9.0. The main attention was placed on dynamic variations in the dominant events and their interactive influences on the global fluid flow in terms of the DMD analysis. Toward this end, a real-time data transfer from the high-speed camera to the arrayed disks was built to enable continuous sampling of the spatiotemporally varying flows at the frequency of 250 Hz for a long run. The spectra of the wall-normal velocity fluctuation, the energy spectra of the DMD modes, and their spatial patterns convincingly determined the energetic unsteady events, i.e., St = 0.051 (Karman vortex street), 0.109 (harmonic event of Karman vortex street) and 0.197 (leading-edge vortex) in the shortest system L/D = 3.0, St = 0.159 (Karman vortex street) and 0.242 (leading-edge vortex) in the system L/D = 6.0, and St = 0.156 (Karman vortex street) and 0.241 (leading-edge vortex) in the longest system L/D = 9.0. In the shortest system L/D = 3.0, the first DMD mode pattern demonstrated intensified entrainment of the massive fluid above and below the whole plate by the Karman vortex street. The phase-dependent variation in the low-order flow field elucidated that this motion was sustained by the consecutive mechanisms of the convective leading-edge vortices near the upper and lower trailing edges, and the large-scale vortical structures occurring immediately behind the trailing edge, whereas the leading-edge vortices were entrained and decayed into the near wake. For the system L/D = 6.0, the closely approximated energy spectra at St = 0.159 and 0.242 indicated the balanced dominance of dual unsteady events in the measurement region. The Karman vortex street was found to induce considerable localized movement of the fluid near the trailing edges of the plate. However, the leading-edge vortices near the trailing edge were found to detach away from the plate and fully decay around 0.5 D behind the trailing edge, where a well-ordered origination of the downstream large-scale vortical structures (the Karman vortex street) was established and might be locally energized by the decayed leading-edge vortex. In the longest system L/D = 9.0, the phase-dependent variations in the low-order flow disclosed a rapid decay of the leading-edge vortices beyond the reattachment zone, reaching the fully diffused state near the trailing edges. Accordingly, no clear signature of the interaction between the Karman vortex street and the leading-edge vortex could be found in the dynamic process of the leading-edge vortex.

Variational Koopman models: Slow collective variables and molecular kinetics from short off-equilibrium simulations

NASA Astrophysics Data System (ADS)

Wu, Hao; Nüske, Feliks; Paul, Fabian; Klus, Stefan; Koltai, Péter; Noé, Frank

2017-04-01

Markov state models (MSMs) and master equation models are popular approaches to approximate molecular kinetics, equilibria, metastable states, and reaction coordinates in terms of a state space discretization usually obtained by clustering. Recently, a powerful generalization of MSMs has been introduced, the variational approach conformation dynamics/molecular kinetics (VAC) and its special case the time-lagged independent component analysis (TICA), which allow us to approximate slow collective variables and molecular kinetics by linear combinations of smooth basis functions or order parameters. While it is known how to estimate MSMs from trajectories whose starting points are not sampled from an equilibrium ensemble, this has not yet been the case for TICA and the VAC. Previous estimates from short trajectories have been strongly biased and thus not variationally optimal. Here, we employ the Koopman operator theory and the ideas from dynamic mode decomposition to extend the VAC and TICA to non-equilibrium data. The main insight is that the VAC and TICA provide a coefficient matrix that we call Koopman model, as it approximates the underlying dynamical (Koopman) operator in conjunction with the basis set used. This Koopman model can be used to compute a stationary vector to reweight the data to equilibrium. From such a Koopman-reweighted sample, equilibrium expectation values and variationally optimal reversible Koopman models can be constructed even with short simulations. The Koopman model can be used to propagate densities, and its eigenvalue decomposition provides estimates of relaxation time scales and slow collective variables for dimension reduction. Koopman models are generalizations of Markov state models, TICA, and the linear VAC and allow molecular kinetics to be described without a cluster discretization.

Analysis of electron beam induced deposition (EBID) of residual hydrocarbons in electron microscopy

NASA Astrophysics Data System (ADS)

Rykaczewski, Konrad; White, William B.; Fedorov, Andrei G.

2007-03-01

In this work we have developed a comprehensive dynamic model of electron beam induced deposition (EBID) of residual hydrocarbon coupling mass transport, electron transport and scattering, and species decomposition to predict the deposition of carbon nanopillars. The simulations predict the local species and electron density distributions, as well as the three-demensional morphology and the growth rate of the deposit. Since the process occurs in a high vacuum environment, surface diffusion is considered as the primary transport mode of surface-adsorbed hydrocarbon precursor. The governing surface transport equation (STE) of the adsorbed species is derived and solved numerically. The transport, scattering, and absorption of primary electron as well as secondary electron generation are treated using the Monte Carlo method. Low energy secondary electrons are the major contributors to hydrocarbon decomposition due to their energy range matching peak dissociation reaction cross section energies for precursor molecules. The deposit and substrate are treated as a continuous entity allowing the simulation of the growth of a realistically sized deposit rather than a large number of cells representing each individual atom as in previously published simulations [Mitsuishi et al., Ultramicroscopy 103, 17 (2005); Silvis-Cividjian, Ph.D. thesis, University of Delft, 2002]. Such formulation allows for simple coupling of the STE with the dynamic growth of the nanopillar. Three different growth regimes occurring in EBID are identified using scaling analysis, and simulations are used to describe the deposit morphology and precursor surface concentration specific for each growth regime.

Exploring the conformational changes of the ATP binding site of gyrase B from Escherichia coli complexed with different established inhibitors by using molecular dynamics simulation: protein-ligand interactions in the light of the alanine scanning and free energy decomposition methods.

PubMed

Saíz-Urra, Liane; Cabrera, Miguel Angel; Froeyen, Matheus

2011-02-01

Currently, bacterial diseases cause a death toll around 2 million people a year encouraging the search for new antimicrobial agents. DNA gyrase is a well-established antibacterial target consisting of two subunits, GyrA and GyrB, in a heterodimer A(2)B(2). GyrA is involved in DNA breakage and reunion and GyrB catalyzes the hydrolysis of ATP. The GyrB subunit from Escherichia coli has been investigated, namely the ATP binding pocket both considering the protein without ligands and bound with the inhibitors clorobiocin, novobiocin and 5'-adenylyl-β-γ-imidodiphosphate. The stability of the systems was studied by molecular dynamics simulation with the further analysis of the time dependent root-mean-square coordinate deviation (RMSD) from the initial structure, and temperature factors. Moreover, exploration of the conformational space of the systems during the MD simulation was carried out by a clustering data mining technique using the average-linkage algorithm. Recognizing the key residues in the binding site of the enzyme that are involved in the binding mode with the aforementioned inhibitors was investigated by using two techniques: free energy decomposition and computational alanine scanning. The results from these simulations highlight the important residues in the ATP binding site and can be useful in the design process of potential new inhibitors. Copyright © 2010 Elsevier Inc. All rights reserved.

Molecular C dynamics downstream: the biochemical decomposition sequence and its impact on soil organic matter structure and function.

PubMed

Grandy, A Stuart; Neff, Jason C

2008-10-15

Advances in spectroscopic and other chemical methods have greatly enhanced our ability to characterize soil organic matter chemistry. As a result, the molecular characteristics of soil C are now known for a range of ecosystems, soil types, and management intensities. Placing this knowledge into a broader ecological and management context is difficult, however, and remains one of the fundamental challenges of soil organic matter research. Here we present a conceptual model of molecular soil C dynamics to stimulate inter-disciplinary research into the ecological implications of molecular C turnover and its management- and process-level controls. Our model describes three properties of soil C dynamics: 1) soil size fractions have unique molecular patterns that reflect varying degrees of biological and physical control over decomposition; 2) there is a common decomposition sequence independent of plant inputs or other ecosystem properties; and 3) molecular decomposition sequences, although consistent, are not uniform and can be altered by processes that accelerate or slow the microbial transformation of specific molecules. The consequences of this model include several key points. First, lignin presents a constraint to decomposition of plant litter and particulate C (>53 microm) but exerts little influence on more stable mineral-associated soil fractions <53 microm. Second, carbon stabilized onto mineral fractions has a distinct composition related more to microbially processed organic matter than to plant-related compounds. Third, disturbances, such as N fertilization and tillage, which alter decomposition rates, can have "downstream effects"; that is, a disturbance that directly alters the molecular dynamics of particulate C may have a series of indirect effects on C stabilization in silt and clay fractions.

Phase-field modeling of diffusional phase behaviors of solid surfaces: A case study of phase-separating Li XFePO 4 electrode particles

DOE PAGES

Heo, Tae Wook; Chen, Long-Qing; Wood, Brandon C.

2015-04-08

In this paper, we present a comprehensive phase-field model for simulating diffusion-mediated kinetic phase behaviors near the surface of a solid particle. The model incorporates elastic inhomogeneity and anisotropy, diffusion mobility anisotropy, interfacial energy anisotropy, and Cahn–Hilliard diffusion kinetics. The free energy density function is formulated based on the regular solution model taking into account the possible solute-surface interaction near the surface. The coherency strain energy is computed using the Fourier-spectral iterative-perturbation method due to the strong elastic inhomogeneity with a zero surface traction boundary condition. Employing a phase-separating Li XFePO 4 electrode particle for Li-ion batteries as a modelmore » system, we perform parametric three-dimensional computer simulations. The model permits the observation of surface phase behaviors that are different from the bulk counterpart. For instance, it reproduces the theoretically well-established surface modes of spinodal decomposition of an unstable solid solution: the surface mode of coherent spinodal decomposition and the surface-directed spinodal decomposition mode. We systematically investigate the influences of major factors on the kinetic surface phase behaviors during the diffusional process. Finally, our simulation study provides insights for tailoring the internal phase microstructure of a particle by controlling the surface phase morphology.« less

A New Method for Nonlinear and Nonstationary Time Series Analysis and Its Application to the Earthquake and Building Response Records

NASA Technical Reports Server (NTRS)

Huang, Norden E.

1999-01-01

A new method for analyzing nonlinear and nonstationary data has been developed. The key part of the method is the Empirical Mode Decomposition method with which any complicated data set can be decomposed into a finite and often small number of Intrinsic Mode Functions (IMF). An IMF is defined as any function having the same numbers of zero-crossing and extrema, and also having symmetric envelopes defined by the local maxima and minima respectively. The IMF also admits well-behaved Hilbert transform. This decomposition method is adaptive, and, therefore, highly efficient. Since the decomposition is based on the local characteristic time scale of the data, it is applicable to nonlinear and nonstationary processes. With the Hilbert transform, the Intrinsic Mode Functions yield instantaneous frequencies as functions of time that give sharp identifications of imbedded structures. The final presentation of the results is an energy-frequency-time distribution, designated as the Hilbert Spectrum, Example of application of this method to earthquake and building response will be given. The results indicate those low frequency components, totally missed by the Fourier analysis, are clearly identified by the new method. Comparisons with Wavelet and window Fourier analysis show the new method offers much better temporal and frequency resolutions.

Identification of significant intrinsic mode functions for the diagnosis of induction motor fault.

PubMed

Cho, Sangjin; Shahriar, Md Rifat; Chong, Uipil

2014-08-01

For the analysis of non-stationary signals generated by a non-linear process like fault of an induction motor, empirical mode decomposition (EMD) is the best choice as it decomposes the signal into its natural oscillatory modes known as intrinsic mode functions (IMFs). However, some of these oscillatory modes obtained from a fault signal are not significant as they do not bear any fault signature and can cause misclassification of the fault instance. To solve this issue, a novel IMF selection algorithm is proposed in this work.

N2-fixing red alder indirectly accelerates ecosystem nitrogen cycling

USGS Publications Warehouse

Perakis, Steven S.; Matkins, Joselin J.; Hibbs, David E.

2012-01-01

Symbiotic N2-fixing tree species can accelerate ecosystem N dynamics through decomposition via direct pathways by producing readily decomposed leaf litter and increasing N supply to decomposers, as well as via indirect pathways by increasing tissue and detrital N in non-fixing vegetation. To evaluate the relative importance of these pathways, we compared three-year decomposition and N dynamics of N2-fixing red alder leaf litter (2.34 %N) to both low-N (0.68 %N) and high-N (1.21 %N) litter of non-fixing Douglas-fir, and decomposed each litter source in four forests dominated by either red alder or Douglas-fir. We also used experimental N fertilization of decomposition plots to assess elevated N availability as a potential mechanism of N2-fixer effects on litter mass loss and N dynamics. Direct effects of N2-fixing red alder on decomposition occurred primarily as faster N release from red alder than Douglas-fir litter, but direct increases in N supply to decomposers via fertilization did not stimulate decomposition of any litter. Fixed N indirectly influenced detrital dynamics by increasing Douglas-fir tissue and litter N concentrations, which accelerated litter N release without accelerating mass loss. By increasing soil N, tissue N, and the rate of N release from litter of non-fixers, we conclude that N2-fixing vegetation can indirectly foster plant-soil feedbacks that contribute to the persistence of elevated N availability in terrestrial ecosystems.

Loblolly pine needle decomposition and nutrient dynamics as affected by irrigation, fertilization, and substrate quality

Treesearch

Felipe G. Sanchez

2001-01-01

This study examined the effects of initial litter quality and irrigation and fertilization treatments on litter decomposition rates and nutrient dynamics (N, Ca, K, Mg, and P) of loblolly (Pinus taeda L.) pine needles in the North Carolina Sand Hills over 3 years. Litter quality was based on the initial C/N ratios, with the high-quality litter having...

Application of decomposition techniques to the preliminary design of a transport aircraft

NASA Technical Reports Server (NTRS)

Rogan, J. E.; Mcelveen, R. P.; Kolb, M. A.

1986-01-01

A multifaceted decomposition of a nonlinear constrained optimization problem describing the preliminary design process for a transport aircraft has been made. Flight dynamics, flexible aircraft loads and deformations, and preliminary structural design subproblems appear prominently in the decomposition. The use of design process decomposition for scheduling design projects, a new system integration approach to configuration control, and the application of object-centered programming to a new generation of design tools are discussed.

Technical Note: Linking climate change and downed woody debris decomposition across forests of the eastern United States

USGS Publications Warehouse

Russell, Matthew B.; Woodall, Christopher W.; D'Amato, Anthony W.; Fraver, Shawn; Bradford, John B.

2014-01-01

Forest ecosystems play a critical role in mitigating greenhouse gas emissions. Forest carbon (C) is stored through photosynthesis and released via decomposition and combustion. Relative to C fixation in biomass, much less is known about C depletion through decomposition of woody debris, particularly under a changing climate. It is assumed that the increased temperatures and longer growing seasons associated with projected climate change will increase the decomposition rates (i.e., more rapid C cycling) of downed woody debris (DWD); however, the magnitude of this increase has not been previously addressed. Using DWD measurements collected from a national forest inventory of the eastern United States, we show that the residence time of DWD may decrease (i.e., more rapid decomposition) by as much as 13% over the next 200 years, depending on various future climate change scenarios and forest types. Although existing dynamic global vegetation models account for the decomposition process, they typically do not include the effect of a changing climate on DWD decomposition rates. We expect that an increased understanding of decomposition rates, as presented in this current work, will be needed to adequately quantify the fate of woody detritus in future forests. Furthermore, we hope these results will lead to improved models that incorporate climate change scenarios for depicting future dead wood dynamics in addition to a traditional emphasis on live-tree demographics.

Thermal Decomposition Mechanism of CL-20 at Different Temperatures by ReaxFF Reactive Molecular Dynamics Simulations.

PubMed

Wang, Fuping; Chen, Lang; Geng, Deshen; Wu, Junying; Lu, Jianying; Wang, Chen

2018-04-26

Hexanitrohexaazaisowurtzitane (CL-20) has a high detonation velocity and pressure, but its sensitivity is also high, which somewhat limits its applications. Therefore, it is important to understand the mechanism and characteristics of thermal decomposition of CL-20. In this study, a ε-CL-20 supercell was constructed and ReaxFF-lg reactive molecular dynamics simulations were performed to investigate thermal decomposition of ε-CL-20 at various temperatures (2000, 2500, 2750, 3000, 3250, and 3500 K). The mechanism of thermal decomposition of CL-20 was analyzed from the aspects of potential energy evolution, the primary reactions, and the intermediate and final product species. The effect of temperature on thermal decomposition of CL-20 is also discussed. The initial reaction path of thermal decomposition of CL-20 is N-NO 2 cleavage to form NO 2 , followed by C-N cleavage, leading to the destruction of the cage structure. A small number of clusters appear in the early reactions and disappear at the end of the reactions. The initial reaction path of CL-20 decomposition is the same at different temperatures. However, as the temperature increases, the decomposition rate of CL-20 increases and the cage structure is destroyed earlier. The temperature greatly affects the rate constants of H 2 O and N 2 , but it has little effect on the rate constants of CO 2 and H 2 .

Partial differential equation-based approach for empirical mode decomposition: application on image analysis.

PubMed

Niang, Oumar; Thioune, Abdoulaye; El Gueirea, Mouhamed Cheikh; Deléchelle, Eric; Lemoine, Jacques

2012-09-01

The major problem with the empirical mode decomposition (EMD) algorithm is its lack of a theoretical framework. So, it is difficult to characterize and evaluate this approach. In this paper, we propose, in the 2-D case, the use of an alternative implementation to the algorithmic definition of the so-called "sifting process" used in the original Huang's EMD method. This approach, especially based on partial differential equations (PDEs), was presented by Niang in previous works, in 2005 and 2007, and relies on a nonlinear diffusion-based filtering process to solve the mean envelope estimation problem. In the 1-D case, the efficiency of the PDE-based method, compared to the original EMD algorithmic version, was also illustrated in a recent paper. Recently, several 2-D extensions of the EMD method have been proposed. Despite some effort, 2-D versions for EMD appear poorly performing and are very time consuming. So in this paper, an extension to the 2-D space of the PDE-based approach is extensively described. This approach has been applied in cases of both signal and image decomposition. The obtained results confirm the usefulness of the new PDE-based sifting process for the decomposition of various kinds of data. Some results have been provided in the case of image decomposition. The effectiveness of the approach encourages its use in a number of signal and image applications such as denoising, detrending, or texture analysis.

Thermal decomposition of the solid phase of nitromethane: ab initio molecular dynamics simulations.

PubMed

Chang, Jing; Lian, Peng; Wei, Dong-Qing; Chen, Xiang-Rong; Zhang, Qing-Ming; Gong, Zi-Zheng

2010-10-29

The Car-Parrinello molecular dynamics simulations were employed to investigate thermal decomposition of the solid nitromethane. It is found that it undergoes chemical decomposition at about 2200 K under ambient pressure. The initiation of reactions involves both proton transfer and commonly known C-N bond cleavage. About 75 species and 100 elementary reactions were observed with the final products being H2O, CO2, N2, and CNCNC. It represents the first complete simulation of solid-phase explosive reactions reported to date, which is of far-reaching implication for design and development of new energetic materials.

EMD-Based Symbolic Dynamic Analysis for the Recognition of Human and Nonhuman Pyroelectric Infrared Signals.

PubMed

Zhao, Jiaduo; Gong, Weiguo; Tang, Yuzhen; Li, Weihong

2016-01-20

In this paper, we propose an effective human and nonhuman pyroelectric infrared (PIR) signal recognition method to reduce PIR detector false alarms. First, using the mathematical model of the PIR detector, we analyze the physical characteristics of the human and nonhuman PIR signals; second, based on the analysis results, we propose an empirical mode decomposition (EMD)-based symbolic dynamic analysis method for the recognition of human and nonhuman PIR signals. In the proposed method, first, we extract the detailed features of a PIR signal into five symbol sequences using an EMD-based symbolization method, then, we generate five feature descriptors for each PIR signal through constructing five probabilistic finite state automata with the symbol sequences. Finally, we use a weighted voting classification strategy to classify the PIR signals with their feature descriptors. Comparative experiments show that the proposed method can effectively classify the human and nonhuman PIR signals and reduce PIR detector's false alarms.

A new position measurement system using a motion-capture camera for wind tunnel tests.

PubMed

Park, Hyo Seon; Kim, Ji Young; Kim, Jin Gi; Choi, Se Woon; Kim, Yousok

2013-09-13

Considering the characteristics of wind tunnel tests, a position measurement system that can minimize the effects on the flow of simulated wind must be established. In this study, a motion-capture camera was used to measure the displacement responses of structures in a wind tunnel test, and the applicability of the system was tested. A motion-capture system (MCS) could output 3D coordinates using two-dimensional image coordinates obtained from the camera. Furthermore, this remote sensing system had some flexibility regarding lab installation because of its ability to measure at relatively long distances from the target structures. In this study, we performed wind tunnel tests on a pylon specimen and compared the measured responses of the MCS with the displacements measured with a laser displacement sensor (LDS). The results of the comparison revealed that the time-history displacement measurements from the MCS slightly exceeded those of the LDS. In addition, we confirmed the measuring reliability of the MCS by identifying the dynamic properties (natural frequency, damping ratio, and mode shape) of the test specimen using system identification methods (frequency domain decomposition, FDD). By comparing the mode shape obtained using the aforementioned methods with that obtained using the LDS, we also confirmed that the MCS could construct a more accurate mode shape (bending-deflection mode shape) with the 3D measurements.

A New Position Measurement System Using a Motion-Capture Camera for Wind Tunnel Tests

PubMed Central

Park, Hyo Seon; Kim, Ji Young; Kim, Jin Gi; Choi, Se Woon; Kim, Yousok

2013-01-01

Considering the characteristics of wind tunnel tests, a position measurement system that can minimize the effects on the flow of simulated wind must be established. In this study, a motion-capture camera was used to measure the displacement responses of structures in a wind tunnel test, and the applicability of the system was tested. A motion-capture system (MCS) could output 3D coordinates using two-dimensional image coordinates obtained from the camera. Furthermore, this remote sensing system had some flexibility regarding lab installation because of its ability to measure at relatively long distances from the target structures. In this study, we performed wind tunnel tests on a pylon specimen and compared the measured responses of the MCS with the displacements measured with a laser displacement sensor (LDS). The results of the comparison revealed that the time-history displacement measurements from the MCS slightly exceeded those of the LDS. In addition, we confirmed the measuring reliability of the MCS by identifying the dynamic properties (natural frequency, damping ratio, and mode shape) of the test specimen using system identification methods (frequency domain decomposition, FDD). By comparing the mode shape obtained using the aforementioned methods with that obtained using the LDS, we also confirmed that the MCS could construct a more accurate mode shape (bending-deflection mode shape) with the 3D measurements. PMID:24064600

Decomposition of Proteins into Dynamic Units from Atomic Cross-Correlation Functions.

PubMed

Calligari, Paolo; Gerolin, Marco; Abergel, Daniel; Polimeno, Antonino

2017-01-10

In this article, we present a clustering method of atoms in proteins based on the analysis of the correlation times of interatomic distance correlation functions computed from MD simulations. The goal is to provide a coarse-grained description of the protein in terms of fewer elements that can be treated as dynamically independent subunits. Importantly, this domain decomposition method does not take into account structural properties of the protein. Instead, the clustering of protein residues in terms of networks of dynamically correlated domains is defined on the basis of the effective correlation times of the pair distance correlation functions. For these properties, our method stands as a complementary analysis to the customary protein decomposition in terms of quasi-rigid, structure-based domains. Results obtained for a prototypal protein structure illustrate the approach proposed.

Uncertainty in the fate of soil organic carbon: A comparison of three conceptually different soil decomposition models

USGS Publications Warehouse

He, Yujie; Yang, Jinyan; Zhuang, Qianlai; McGuire, A. David; Zhu, Qing; Liu, Yaling; Teskey, Robert O.

2014-01-01

Conventional Q10 soil organic matter decomposition models and more complex microbial models are available for making projections of future soil carbon dynamics. However, it is unclear (1) how well the conceptually different approaches can simulate observed decomposition and (2) to what extent the trajectories of long-term simulations differ when using the different approaches. In this study, we compared three structurally different soil carbon (C) decomposition models (one Q10 and two microbial models of different complexity), each with a one- and two-horizon version. The models were calibrated and validated using 4 years of measurements of heterotrophic soil CO2 efflux from trenched plots in a Dahurian larch (Larix gmelinii Rupr.) plantation. All models reproduced the observed heterotrophic component of soil CO2 efflux, but the trajectories of soil carbon dynamics differed substantially in 100 year simulations with and without warming and increased litterfall input, with microbial models that produced better agreement with observed changes in soil organic C in long-term warming experiments. Our results also suggest that both constant and varying carbon use efficiency are plausible when modeling future decomposition dynamics and that the use of a short-term (e.g., a few years) period of measurement is insufficient to adequately constrain model parameters that represent long-term responses of microbial thermal adaption. These results highlight the need to reframe the representation of decomposition models and to constrain parameters with long-term observations and multiple data streams. We urge caution in interpreting future soil carbon responses derived from existing decomposition models because both conceptual and parameter uncertainties are substantial.

Application of decomposition techniques to the preliminary design of a transport aircraft

NASA Technical Reports Server (NTRS)

Rogan, J. E.; Kolb, M. A.

1987-01-01

A nonlinear constrained optimization problem describing the preliminary design process for a transport aircraft has been formulated. A multifaceted decomposition of the optimization problem has been made. Flight dynamics, flexible aircraft loads and deformations, and preliminary structural design subproblems appear prominently in the decomposition. The use of design process decomposition for scheduling design projects, a new system integration approach to configuration control, and the application of object-centered programming to a new generation of design tools are discussed.

Leaf litter decomposition and elemental change in three Appalachian mountain streams of different pH

Treesearch

Steven W. Solada; Sue A. Perry; William B. Perry

1996-01-01

The decomposition of leaf litter provides the primary nutrient source for many of the headwater mountain streams in forested catchments. An investigation of factors affected by global change that influence organic matter decomposition, such as temperature and pH, is important in understanding the dynamics of these systems. We conducted a study of leaf litter elemental...

Temperature and soil organic matter decomposition rates - synthesis of current knowledge and a way forward

Treesearch

Richard T. Conant; Michael Ryan; Goran I. Agren; Hannah E. Birge; Eric A. Davidson; Peter E. Eliasson; Sarah E. Evans; Serita D. Frey; Christian P. Giardina; Francesca M. Hopkins; Riitta Hyvonen; Miko U. F . Kirschbaum; Jocelyn M. Lavallee; Jens Leifeld; William J. Parton; Jessica Megan Steinweg; Matthew D. Wallenstein; J . A. Martin Wetterstedt; Mark A. Bradford

2011-01-01

The response of soil organic matter (OM) decomposition to increasing temperature is a critical aspect of ecosystem responses to global change. The impacts of climate warming on decomposition dynamics have not been resolved due to apparently contradictory results from field and lab experiments, most of which has focused on labile carbon with short turnover times. But...

Identification of sudden stiffness changes in the acceleration response of a bridge to moving loads using ensemble empirical mode decomposition

NASA Astrophysics Data System (ADS)

Aied, H.; González, A.; Cantero, D.

2016-01-01

The growth of heavy traffic together with aggressive environmental loads poses a threat to the safety of an aging bridge stock. Often, damage is only detected via visual inspection at a point when repairing costs can be quite significant. Ideally, bridge managers would want to identify a stiffness change as soon as possible, i.e., as it is occurring, to plan for prompt measures before reaching a prohibitive cost. Recent developments in signal processing techniques such as wavelet analysis and empirical mode decomposition (EMD) have aimed to address this need by identifying a stiffness change from a localised feature in the structural response to traffic. However, the effectiveness of these techniques is limited by the roughness of the road profile, the vehicle speed and the noise level. In this paper, ensemble empirical mode decomposition (EEMD) is applied by the first time to the acceleration response of a bridge model to a moving load with the purpose of capturing sudden stiffness changes. EEMD is more adaptive and appears to be better suited to non-linear signals than wavelets, and it reduces the mode mixing problem present in EMD. EEMD is tested in a variety of theoretical 3D vehicle-bridge interaction scenarios. Stiffness changes are successfully identified, even for small affected regions, relatively poor profiles, high vehicle speeds and significant noise. The latter is due to the ability of EEMD to separate high frequency components associated to sudden stiffness changes from other frequency components associated to the vehicle-bridge interaction system.

Investigating carbon dynamics in Siberian peat bogs using molecular-level analyses

NASA Astrophysics Data System (ADS)

Kaiser, K.; Benner, R. H.

2013-12-01

Total hydrolysable carbohydrates, and lignin and cutin acid compounds were analyzed in peat cores collected 56.8 N (SIB04), 58.4 N (SIB06), 63.8 N (G137) and 66.5 N (E113) in the Western Siberian Lowland to investigate vegetation, chemical compositions and the stage of decomposition. Sphagnum mosses dominated peatland vegetation in all four cores. High-resolution molecular analyses revealed rapid vegetation changes on timescales of 50-200 years in the southern cores Sib4 and Sib6. Syringyl and vanillyl (S/V) ratios and cutin acids indicated these vegetation changes were due to varying inputs of angiosperm and gymnosperm and root material. In the G137 and E113 cores lichens briefly replaced sphagnum mosses and vascular plants. Molecular decomposition indicators used in this study tracked the decomposition of different organic constituents of peat organic matter. The carbohydrate decomposition index was sensitive to the polysaccharide component of all peat-forming plants, whereas acid/aldehyde ratios of S and V phenols (Ac/AlS,V) followed the lignin component of vascular plants. Low carbohydrate decomposition indices in peat layers corresponded well with elevated (Ad/Al)S,V ratios. This suggested both classes of biochemicals were simultaneously decomposed, and decomposition processes were associated with extensive total mass loss in these ombrotrophic systems. Selective decomposition or transformation of lignin was observed in the permafrost-influenced northern cores G137 and E113. Both cores exhibited the highest (Ad/Al)S,V ratios, almost four-fold higher than measured in peat-forming plants. The extent of decomposition in the four peat cores did not uniformly increase with age, but showed episodic extensive decomposition events. Variable decomposition events independent of climatic conditions and vegetation shifts highlight the complexity of peatland dynamics.

Technical Note: Linking climate change and downed woody debris decomposition across forests of the eastern United States

NASA Astrophysics Data System (ADS)

Russell, M. B.; Woodall, C. W.; D'Amato, A. W.; Fraver, S.; Bradford, J. B.

2014-06-01

Forest ecosystems play a critical role in mitigating greenhouse gas emissions. Long-term forest carbon (C) storage is determined by the balance between C fixation into biomass through photosynthesis and C release via decomposition and combustion. Relative to C fixation in biomass, much less is known about C depletion through decomposition of woody debris, particularly under a changing climate. It is assumed that the increased temperatures and longer growing seasons associated with projected climate change will increase the decomposition rates (i.e., more rapid C cycling) of downed woody debris (DWD); however, the magnitude of this increase has not been previously addressed. Using DWD measurements collected from a national forest inventory of the eastern United States, we show that the residence time of DWD may decrease (i.e., more rapid decomposition) by as much as 13% over the next 200 years depending on various future climate change scenarios and forest types. Although existing dynamic global vegetation models account for the decomposition process, they typically do not include the effect of a changing climate on DWD decomposition rates. We expect that an increased understanding of decomposition rates, as presented in this current work, will be needed to adequately quantify the fate of woody detritus in future forests. Furthermore, we hope these results will lead to improved models that incorporate climate change scenarios for depicting future dead wood dynamics, in addition to a traditional emphasis on live tree demographics.

Digital-analog quantum simulation of generalized Dicke models with superconducting circuits

NASA Astrophysics Data System (ADS)

Lamata, Lucas

2017-03-01

We propose a digital-analog quantum simulation of generalized Dicke models with superconducting circuits, including Fermi- Bose condensates, biased and pulsed Dicke models, for all regimes of light-matter coupling. We encode these classes of problems in a set of superconducting qubits coupled with a bosonic mode implemented by a transmission line resonator. Via digital-analog techniques, an efficient quantum simulation can be performed in state-of-the-art circuit quantum electrodynamics platforms, by suitable decomposition into analog qubit-bosonic blocks and collective single-qubit pulses through digital steps. Moreover, just a single global analog block would be needed during the whole protocol in most of the cases, superimposed with fast periodic pulses to rotate and detune the qubits. Therefore, a large number of digital steps may be attained with this approach, providing a reduced digital error. Additionally, the number of gates per digital step does not grow with the number of qubits, rendering the simulation efficient. This strategy paves the way for the scalable digital-analog quantum simulation of many-body dynamics involving bosonic modes and spin degrees of freedom with superconducting circuits.

Results on SSH neural network forecasting in the Mediterranean Sea

NASA Astrophysics Data System (ADS)

Rixen, Michel; Beckers, Jean-Marie; Alvarez, Alberto; Tintore, Joaquim

2002-01-01

Nowadays, satellites are the only monitoring systems that cover almost continuously all possible ocean areas and are now an essential part of operational oceanography. A novel approach based on artificial intelligence (AI) concepts, exploits pasts time series of satellite images to infer near future ocean conditions at the surface by neural networks and genetic algorithms. The size of the AI problem is drastically reduced by splitting the spatio-temporal variability contained in the remote sensing data by using empirical orthogonal function (EOF) decomposition. The problem of forecasting the dynamics of a 2D surface field can thus be reduced by selecting the most relevant empirical modes, and non-linear time series predictors are then applied on the amplitudes only. In the present case study, we use altimetric maps of the Mediterranean Sea, combining TOPEX-POSEIDON and ERS-1/2 data for the period 1992 to 1997. The learning procedure is applied to each mode individually. The final forecast is then reconstructed form the EOFs and the forecasted amplitudes and compared to the real observed field for validation of the method.

System Identification and POD Method Applied to Unsteady Aerodynamics

NASA Technical Reports Server (NTRS)

Tang, Deman; Kholodar, Denis; Juang, Jer-Nan; Dowell, Earl H.

2001-01-01

The representation of unsteady aerodynamic flow fields in terms of global aerodynamic modes has proven to be a useful method for reducing the size of the aerodynamic model over those representations that use local variables at discrete grid points in the flow field. Eigenmodes and Proper Orthogonal Decomposition (POD) modes have been used for this purpose with good effect. This suggests that system identification models may also be used to represent the aerodynamic flow field. Implicit in the use of a systems identification technique is the notion that a relative small state space model can be useful in describing a dynamical system. The POD model is first used to show that indeed a reduced order model can be obtained from a much larger numerical aerodynamical model (the vortex lattice method is used for illustrative purposes) and the results from the POD and the system identification methods are then compared. For the example considered, the two methods are shown to give comparable results in terms of accuracy and reduced model size. The advantages and limitations of each approach are briefly discussed. Both appear promising and complementary in their characteristics.

Digital-analog quantum simulation of generalized Dicke models with superconducting circuits

PubMed Central

Lamata, Lucas

2017-01-01

We propose a digital-analog quantum simulation of generalized Dicke models with superconducting circuits, including Fermi- Bose condensates, biased and pulsed Dicke models, for all regimes of light-matter coupling. We encode these classes of problems in a set of superconducting qubits coupled with a bosonic mode implemented by a transmission line resonator. Via digital-analog techniques, an efficient quantum simulation can be performed in state-of-the-art circuit quantum electrodynamics platforms, by suitable decomposition into analog qubit-bosonic blocks and collective single-qubit pulses through digital steps. Moreover, just a single global analog block would be needed during the whole protocol in most of the cases, superimposed with fast periodic pulses to rotate and detune the qubits. Therefore, a large number of digital steps may be attained with this approach, providing a reduced digital error. Additionally, the number of gates per digital step does not grow with the number of qubits, rendering the simulation efficient. This strategy paves the way for the scalable digital-analog quantum simulation of many-body dynamics involving bosonic modes and spin degrees of freedom with superconducting circuits. PMID:28256559

Importance of soil thermal dynamics on land carbon sequestration in Northern Eurasia during the 21st century

NASA Astrophysics Data System (ADS)

Kicklighter, David; Monier, Erwan; Sokolov, Andrei; Zhuang, Qianlai; Melillo, Jerry

2015-04-01

Recent modeling studies have suggested that carbon sinks in pan-arctic ecosystems may be weakening partially as a result of warming-induced increases in soil organic matter (SOM) decomposition and the exposure of previously frozen SOM to decomposition. This weakening of carbon sinks is likely to continue in the future as vast amount of carbon in permafrost soils is vulnerable to thaw. Here, we examine the importance of considering soil thermal dynamics when determining the effects of climate change and land-use change on carbon dynamics in Northern Eurasia during the 21st century. This importance is assessed by comparing results for a "business as usual" scenario between a version of the Terrestrial Ecosystem Model that does not consider soil thermal dynamics (TEM 4.4) and a version that does consider these dynamics (TEM 6.0). In this scenario, which is similar to the IPCC Representative Concentration Pathways (RCP) 8.5 scenario, the net area covered by food crops and pastures in Northern Eurasia is assumed to remain relatively constant over the 21st century, but the area covered by secondary forests is projected to double as a result of timber harvest and the abandonment of land associated with displacement of agricultural land. Enhanced decomposition from the newly exposed SOM from permafrost thaw also increases nitrogen availability for plant production so that the loss of carbon from the enhanced decomposition is partially compensated by enhanced uptake and storage of atmospheric carbon dioxide in vegetation. Our results indicate that consideration of soil thermal dynamics have a large influence on how simulated terrestrial carbon dynamics in Northern Eurasia respond to changes in climate, atmospheric chemistry (e.g., carbon dioxide fertilization, ozone pollution, nitrogen deposition) and disturbances.

Iterative variational mode decomposition based automated detection of glaucoma using fundus images.

PubMed

Maheshwari, Shishir; Pachori, Ram Bilas; Kanhangad, Vivek; Bhandary, Sulatha V; Acharya, U Rajendra

2017-09-01

Glaucoma is one of the leading causes of permanent vision loss. It is an ocular disorder caused by increased fluid pressure within the eye. The clinical methods available for the diagnosis of glaucoma require skilled supervision. They are manual, time consuming, and out of reach of common people. Hence, there is a need for an automated glaucoma diagnosis system for mass screening. In this paper, we present a novel method for an automated diagnosis of glaucoma using digital fundus images. Variational mode decomposition (VMD) method is used in an iterative manner for image decomposition. Various features namely, Kapoor entropy, Renyi entropy, Yager entropy, and fractal dimensions are extracted from VMD components. ReliefF algorithm is used to select the discriminatory features and these features are then fed to the least squares support vector machine (LS-SVM) for classification. Our proposed method achieved classification accuracies of 95.19% and 94.79% using three-fold and ten-fold cross-validation strategies, respectively. This system can aid the ophthalmologists in confirming their manual reading of classes (glaucoma or normal) using fundus images. Copyright © 2017 Elsevier Ltd. All rights reserved.

Speech rhythm analysis with decomposition of the amplitude envelope: characterizing rhythmic patterns within and across languages.

PubMed

Tilsen, Sam; Arvaniti, Amalia

2013-07-01

This study presents a method for analyzing speech rhythm using empirical mode decomposition of the speech amplitude envelope, which allows for extraction and quantification of syllabic- and supra-syllabic time-scale components of the envelope. The method of empirical mode decomposition of a vocalic energy amplitude envelope is illustrated in detail, and several types of rhythm metrics derived from this method are presented. Spontaneous speech extracted from the Buckeye Corpus is used to assess the effect of utterance length on metrics, and it is shown how metrics representing variability in the supra-syllabic time-scale components of the envelope can be used to identify stretches of speech with targeted rhythmic characteristics. Furthermore, the envelope-based metrics are used to characterize cross-linguistic differences in speech rhythm in the UC San Diego Speech Lab corpus of English, German, Greek, Italian, Korean, and Spanish speech elicited in read sentences, read passages, and spontaneous speech. The envelope-based metrics exhibit significant effects of language and elicitation method that argue for a nuanced view of cross-linguistic rhythm patterns.

Long-term dynamics of pine and hardwood litter in contrasting environments: Toward a global model of decomposition

Treesearch

Henry L. Gholz; David A. Wedin; Stephen M. Smitherman; Mark E. Harmon; William J. Parton

2000-01-01

We analysed data on mass loss after five years of decomposition in the field from both fine root and leaf litters from two highly contrasting trees, Drypetes glallca, a tropical hardwood tree from Puerto Rico, and pine species from North America as part of the Long-Term Intersite Decomposition Experiment (LIDET). LIDET is a reciprocal litterbag study...

A data-driven decomposition approach to model aerodynamic forces on flapping airfoils

NASA Astrophysics Data System (ADS)

Raiola, Marco; Discetti, Stefano; Ianiro, Andrea

2017-11-01

In this work, we exploit a data-driven decomposition of experimental data from a flapping airfoil experiment with the aim of isolating the main contributions to the aerodynamic force and obtaining a phenomenological model. Experiments are carried out on a NACA 0012 airfoil in forward flight with both heaving and pitching motion. Velocity measurements of the near field are carried out with Planar PIV while force measurements are performed with a load cell. The phase-averaged velocity fields are transformed into the wing-fixed reference frame, allowing for a description of the field in a domain with fixed boundaries. The decomposition of the flow field is performed by means of the POD applied on the velocity fluctuations and then extended to the phase-averaged force data by means of the Extended POD approach. This choice is justified by the simple consideration that aerodynamic forces determine the largest contributions to the energetic balance in the flow field. Only the first 6 modes have a relevant contribution to the force. A clear relationship can be drawn between the force and the flow field modes. Moreover, the force modes are closely related (yet slightly different) to the contributions of the classic potential models in literature, allowing for their correction. This work has been supported by the Spanish MINECO under Grant TRA2013-41103-P.

Applications of Hilbert Spectral Analysis for Speech and Sound Signals

NASA Technical Reports Server (NTRS)

Huang, Norden E.

2003-01-01

A new method for analyzing nonlinear and nonstationary data has been developed, and the natural applications are to speech and sound signals. The key part of the method is the Empirical Mode Decomposition method with which any complicated data set can be decomposed into a finite and often small number of Intrinsic Mode Functions (IMF). An IMF is defined as any function having the same numbers of zero-crossing and extrema, and also having symmetric envelopes defined by the local maxima and minima respectively. The IMF also admits well-behaved Hilbert transform. This decomposition method is adaptive, and, therefore, highly efficient. Since the decomposition is based on the local characteristic time scale of the data, it is applicable to nonlinear and nonstationary processes. With the Hilbert transform, the Intrinsic Mode Functions yield instantaneous frequencies as functions of time, which give sharp identifications of imbedded structures. This method invention can be used to process all acoustic signals. Specifically, it can process the speech signals for Speech synthesis, Speaker identification and verification, Speech recognition, and Sound signal enhancement and filtering. Additionally, as the acoustical signals from machinery are essentially the way the machines are talking to us. Therefore, the acoustical signals, from the machines, either from sound through air or vibration on the machines, can tell us the operating conditions of the machines. Thus, we can use the acoustic signal to diagnosis the problems of machines.

Padé spectrum decompositions of quantum distribution functions and optimal hierarchical equations of motion construction for quantum open systems

NASA Astrophysics Data System (ADS)

Hu, Jie; Luo, Meng; Jiang, Feng; Xu, Rui-Xue; Yan, YiJing

2011-06-01

Padé spectrum decomposition is an optimal sum-over-poles expansion scheme of Fermi function and Bose function [J. Hu, R. X. Xu, and Y. J. Yan, J. Chem. Phys. 133, 101106 (2010)], 10.1063/1.3484491. In this work, we report two additional members to this family, from which the best among all sum-over-poles methods could be chosen for different cases of application. Methods are developed for determining these three Padé spectrum decomposition expansions at machine precision via simple algorithms. We exemplify the applications of present development with optimal construction of hierarchical equations-of-motion formulations for nonperturbative quantum dissipation and quantum transport dynamics. Numerical demonstrations are given for two systems. One is the transient transport current to an interacting quantum-dots system, together with the involved high-order co-tunneling dynamics. Another is the non-Markovian dynamics of a spin-boson system.

Fault identification of rotor-bearing system based on ensemble empirical mode decomposition and self-zero space projection analysis

NASA Astrophysics Data System (ADS)

Jiang, Fan; Zhu, Zhencai; Li, Wei; Zhou, Gongbo; Chen, Guoan

2014-07-01

Accurately identifying faults in rotor-bearing systems by analyzing vibration signals, which are nonlinear and nonstationary, is challenging. To address this issue, a new approach based on ensemble empirical mode decomposition (EEMD) and self-zero space projection analysis is proposed in this paper. This method seeks to identify faults appearing in a rotor-bearing system using simple algebraic calculations and projection analyses. First, EEMD is applied to decompose the collected vibration signals into a set of intrinsic mode functions (IMFs) for features. Second, these extracted features under various mechanical health conditions are used to design a self-zero space matrix according to space projection analysis. Finally, the so-called projection indicators are calculated to identify the rotor-bearing system's faults with simple decision logic. Experiments are implemented to test the reliability and effectiveness of the proposed approach. The results show that this approach can accurately identify faults in rotor-bearing systems.

The Removal of EOG Artifacts From EEG Signals Using Independent Component Analysis and Multivariate Empirical Mode Decomposition.

PubMed

Wang, Gang; Teng, Chaolin; Li, Kuo; Zhang, Zhonglin; Yan, Xiangguo

2016-09-01

The recorded electroencephalography (EEG) signals are usually contaminated by electrooculography (EOG) artifacts. In this paper, by using independent component analysis (ICA) and multivariate empirical mode decomposition (MEMD), the ICA-based MEMD method was proposed to remove EOG artifacts (EOAs) from multichannel EEG signals. First, the EEG signals were decomposed by the MEMD into multiple multivariate intrinsic mode functions (MIMFs). The EOG-related components were then extracted by reconstructing the MIMFs corresponding to EOAs. After performing the ICA of EOG-related signals, the EOG-linked independent components were distinguished and rejected. Finally, the clean EEG signals were reconstructed by implementing the inverse transform of ICA and MEMD. The results of simulated and real data suggested that the proposed method could successfully eliminate EOAs from EEG signals and preserve useful EEG information with little loss. By comparing with other existing techniques, the proposed method achieved much improvement in terms of the increase of signal-to-noise and the decrease of mean square error after removing EOAs.

Noise-assisted data processing with empirical mode decomposition in biomedical signals.

PubMed

Karagiannis, Alexandros; Constantinou, Philip

2011-01-01

In this paper, a methodology is described in order to investigate the performance of empirical mode decomposition (EMD) in biomedical signals, and especially in the case of electrocardiogram (ECG). Synthetic ECG signals corrupted with white Gaussian noise are employed and time series of various lengths are processed with EMD in order to extract the intrinsic mode functions (IMFs). A statistical significance test is implemented for the identification of IMFs with high-level noise components and their exclusion from denoising procedures. Simulation campaign results reveal that a decrease of processing time is accomplished with the introduction of preprocessing stage, prior to the application of EMD in biomedical time series. Furthermore, the variation in the number of IMFs according to the type of the preprocessing stage is studied as a function of SNR and time-series length. The application of the methodology in MIT-BIH ECG records is also presented in order to verify the findings in real ECG signals.

[A Feature Extraction Method for Brain Computer Interface Based on Multivariate Empirical Mode Decomposition].

PubMed

Wang, Jinjia; Liu, Yuan

2015-04-01

This paper presents a feature extraction method based on multivariate empirical mode decomposition (MEMD) combining with the power spectrum feature, and the method aims at the non-stationary electroencephalogram (EEG) or magnetoencephalogram (MEG) signal in brain-computer interface (BCI) system. Firstly, we utilized MEMD algorithm to decompose multichannel brain signals into a series of multiple intrinsic mode function (IMF), which was proximate stationary and with multi-scale. Then we extracted and reduced the power characteristic from each IMF to a lower dimensions using principal component analysis (PCA). Finally, we classified the motor imagery tasks by linear discriminant analysis classifier. The experimental verification showed that the correct recognition rates of the two-class and four-class tasks of the BCI competition III and competition IV reached 92.0% and 46.2%, respectively, which were superior to the winner of the BCI competition. The experimental proved that the proposed method was reasonably effective and stable and it would provide a new way for feature extraction.

Equivalence between a generalized dendritic network and a set of one-dimensional networks as a ground of linear dynamics.

PubMed

Koda, Shin-ichi

2015-05-28

It has been shown by some existing studies that some linear dynamical systems defined on a dendritic network are equivalent to those defined on a set of one-dimensional networks in special cases and this transformation to the simple picture, which we call linear chain (LC) decomposition, has a significant advantage in understanding properties of dendrimers. In this paper, we expand the class of LC decomposable system with some generalizations. In addition, we propose two general sufficient conditions for LC decomposability with a procedure to systematically realize the LC decomposition. Some examples of LC decomposable linear dynamical systems are also presented with their graphs. The generalization of the LC decomposition is implemented in the following three aspects: (i) the type of linear operators; (ii) the shape of dendritic networks on which linear operators are defined; and (iii) the type of symmetry operations representing the symmetry of the systems. In the generalization (iii), symmetry groups that represent the symmetry of dendritic systems are defined. The LC decomposition is realized by changing the basis of a linear operator defined on a dendritic network into bases of irreducible representations of the symmetry group. The achievement of this paper makes it easier to utilize the LC decomposition in various cases. This may lead to a further understanding of the relation between structure and functions of dendrimers in future studies.

Towards a paradigm shift in the modeling of soil organic carbon decomposition for earth system models

NASA Astrophysics Data System (ADS)

He, Yujie

Soils are the largest terrestrial carbon pools and contain approximately 2200 Pg of carbon. Thus, the dynamics of soil carbon plays an important role in the global carbon cycle and climate system. Earth System Models are used to project future interactions between terrestrial ecosystem carbon dynamics and climate. However, these models often predict a wide range of soil carbon responses and their formulations have lagged behind recent soil science advances, omitting key biogeochemical mechanisms. In contrast, recent mechanistically-based biogeochemical models that explicitly account for microbial biomass pools and enzyme kinetics that catalyze soil carbon decomposition produce notably different results and provide a closer match to recent observations. However, a systematic evaluation of the advantages and disadvantages of the microbial models and how they differ from empirical, first-order formulations in soil decomposition models for soil organic carbon is still needed. This dissertation consists of a series of model sensitivity and uncertainty analyses and identifies dominant decomposition processes in determining soil organic carbon dynamics. Poorly constrained processes or parameters that require more experimental data integration are also identified. This dissertation also demonstrates the critical role of microbial life-history traits (e.g. microbial dormancy) in the modeling of microbial activity in soil organic matter decomposition models. Finally, this study surveys and synthesizes a number of recently published microbial models and provides suggestions for future microbial model developments.

Decomposition and nitrogen dynamics of 15N-labeled leaf, root, and twig litter in temperate coniferous forests

USGS Publications Warehouse

van Huysen, Tiff L.; Harmon, Mark E.; Perakis, Steven S.; Chen, Hua

2013-01-01

Litter nutrient dynamics contribute significantly to biogeochemical cycling in forest ecosystems. We examined how site environment and initial substrate quality influence decomposition and nitrogen (N) dynamics of multiple litter types. A 2.5-year decomposition study was installed in the Oregon Coast Range and West Cascades using 15N-labeled litter from Acer macrophyllum, Picea sitchensis, and Pseudotsuga menziesii. Mass loss for leaf litter was similar between the two sites, while root and twig litter exhibited greater mass loss in the Coast Range. Mass loss was greatest from leaves and roots, and species differences in mass loss were more prominent in the Coast Range. All litter types and species mineralized N early in the decomposition process; only A. macrophyllum leaves exhibited a net N immobilization phase. There were no site differences with respect to litter N dynamics despite differences in site N availability, and litter N mineralization patterns were species-specific. For multiple litter × species combinations, the difference between gross and net N mineralization was significant, and gross mineralization was 7–20 % greater than net mineralization. The mineralization results suggest that initial litter chemistry may be an important driver of litter N dynamics. Our study demonstrates that greater amounts of N are cycling through these systems than may be quantified by only measuring net mineralization and challenges current leaf-based biogeochemical theory regarding patterns of N immobilization and mineralization.

Time-frequency dynamics of resting-state brain connectivity measured with fMRI.

PubMed

Chang, Catie; Glover, Gary H

2010-03-01

Most studies of resting-state functional connectivity using fMRI employ methods that assume temporal stationarity, such as correlation and data-driven decompositions computed across the duration of the scan. However, evidence from both task-based fMRI studies and animal electrophysiology suggests that functional connectivity may exhibit dynamic changes within time scales of seconds to minutes. In the present study, we investigated the dynamic behavior of resting-state connectivity across the course of a single scan, performing a time-frequency coherence analysis based on the wavelet transform. We focused on the connectivity of the posterior cingulate cortex (PCC), a primary node of the default-mode network, examining its relationship with both the "anticorrelated" ("task-positive") network as well as other nodes of the default-mode network. It was observed that coherence and phase between the PCC and the anticorrelated network was variable in time and frequency, and statistical testing based on Monte Carlo simulations revealed the presence of significant scale-dependent temporal variability. In addition, a sliding-window correlation procedure identified other regions across the brain that exhibited variable connectivity with the PCC across the scan, which included areas previously implicated in attention and salience processing. Although it is unclear whether the observed coherence and phase variability can be attributed to residual noise or modulation of cognitive state, the present results illustrate that resting-state functional connectivity is not static, and it may therefore prove valuable to consider measures of variability, in addition to average quantities, when characterizing resting-state networks. Copyright (c) 2009 Elsevier Inc. All rights reserved.

Low-Temperature Aging Characteristics of Type 316L Stainless Steel Welds: Dependence on Solidification Mode

NASA Astrophysics Data System (ADS)

Abe, Hiroshi; Watanabe, Yutaka

2008-06-01

Thermal aging embrittlement of light water reactor (LWR) components made of stainless steel cast has been recognized as a potential degradation issue, and careful attention has been paid to it. Although welds of austenitic stainless steels have γ-δ duplex microstructure, which is similar to that of the stainless steel cast, examination of the thermal aging characteristics of the stainless steel welds is very limited. In this investigation, two types of type 316L stainless steel weld metal with different solidification modes were prepared using two kinds of filler metals having tailored Ni equivalent and Cr equivalent. Differences between the two weld metals in the morphology of microstructure, in the composition of δ-ferrite, and in hardening behaviors with isothermal aging at 335 °C have been investigated. The hardness of the ferrite phase has increased with aging time, while the hardness of austenite phase has stayed the same. The mottled aspect has been observed in δ-ferrite of aged samples by transmission electron microscopy (TEM) observation. These characteristics suggest that spinodal decomposition has occurred in δ-ferrite by aging at 335 °C. The age-hardening rate of δ-ferrite was faster for the primary austenite solidification mode (AF mode) sample than the primary ferrite solidification mode (FA mode) sample in the initial stage of the aging up to 2000 hours. It has been suggested that the solidification mode can affect the kinetics of spinodal decomposition.

Multi-scale fluctuation analysis of precipitation in Beijing by Extreme-point Symmetric Mode Decomposition

NASA Astrophysics Data System (ADS)

Li, Jiqing; Duan, Zhipeng; Huang, Jing

2018-06-01

With the aggravation of the global climate change, the shortage of water resources in China is becoming more and more serious. Using reasonable methods to study changes in precipitation is very important for planning and management of water resources. Based on the time series of precipitation in Beijing from 1951 to 2015, the multi-scale features of precipitation are analyzed by the Extreme-point Symmetric Mode Decomposition (ESMD) method to forecast the precipitation shift. The results show that the precipitation series have periodic changes of 2.6, 4.3, 14 and 21.7 years, and the variance contribution rate of each modal component shows that the inter-annual variation dominates the precipitation in Beijing. It is predicted that precipitation in Beijing will continue to decrease in the near future.

A novel approach for baseline correction in 1H-MRS signals based on ensemble empirical mode decomposition.

PubMed

Parto Dezfouli, Mohammad Ali; Dezfouli, Mohsen Parto; Rad, Hamidreza Saligheh

2014-01-01

Proton magnetic resonance spectroscopy ((1)H-MRS) is a non-invasive diagnostic tool for measuring biochemical changes in the human body. Acquired (1)H-MRS signals may be corrupted due to a wideband baseline signal generated by macromolecules. Recently, several methods have been developed for the correction of such baseline signals, however most of them are not able to estimate baseline in complex overlapped signal. In this study, a novel automatic baseline correction method is proposed for (1)H-MRS spectra based on ensemble empirical mode decomposition (EEMD). This investigation was applied on both the simulated data and the in-vivo (1)H-MRS of human brain signals. Results justify the efficiency of the proposed method to remove the baseline from (1)H-MRS signals.

Temporal structure of neuronal population oscillations with empirical model decomposition

NASA Astrophysics Data System (ADS)

Li, Xiaoli

2006-08-01

Frequency analysis of neuronal oscillation is very important for understanding the neural information processing and mechanism of disorder in the brain. This Letter addresses a new method to analyze the neuronal population oscillations with empirical mode decomposition (EMD). Following EMD of neuronal oscillation, a series of intrinsic mode functions (IMFs) are obtained, then Hilbert transform of IMFs can be used to extract the instantaneous time frequency structure of neuronal oscillation. The method is applied to analyze the neuronal oscillation in the hippocampus of epileptic rats in vivo, the results show the neuronal oscillations have different descriptions during the pre-ictal, seizure onset and ictal periods of the epileptic EEG at the different frequency band. This new method is very helpful to provide a view for the temporal structure of neural oscillation.

Direct numerical simulation of supersonic turbulent boundary layer subjected to a curved compression ramp

NASA Astrophysics Data System (ADS)

Tong, Fulin; Li, Xinliang; Duan, Yanhui; Yu, Changping

2017-12-01

Numerical investigations on a supersonic turbulent boundary layer over a longitudinal curved compression ramp are conducted using direct numerical simulation for a free stream Mach number M∞ = 2.9 and Reynolds number Reθ = 2300. The total turning angle is 24°, and the concave curvature radius is 15 times the thickness of the incoming turbulent boundary layer. Under the selected conditions, the shock foot is transferred to a fan of the compression wave because of the weaker adverse pressure gradient. The time-averaged flow-field in the curved ramp is statistically attached where the instantaneous flow-field is close to the intermittent transitory detachment state. Studies on coherent vortex structures have shown that large-scale vortex packets are enhanced significantly when the concave curvature is aligned in the spanwise direction. Consistent with findings of previous experiments, the effect of the concave curvature on the logarithmic region of the mean velocity profiles is found to be small. The intensity of the turbulent fluctuations is amplified across the curved ramp. Based on the analysis of the Reynolds stress anisotropy tensor, the evolutions of the turbulence state in the inner and outer layers of the boundary layer are considerably different. The curvature effect on the transport mechanism of the turbulent kinetic energy is studied using the balance analysis of the contributing terms in the transport equation. Furthermore, the Görtler instability in the curved ramp is quantitatively analyzed using a stability criterion. The instantaneous streamwise vorticity confirms the existence of the Görtler-like structures. These structures are characterized by an unsteady motion. In addition, the dynamic mode decomposition analysis of the instantaneous flow field at the spanwise/wall-normal plane reveals that four dynamical relevant modes with performance loss of 16% provide an optimal low-order representation of the essential characteristics of the numerical data. The spatial structures of the dominated low-frequency dynamic modes are found to be similar to that of the Görtler-like vortices.

The dynamic bacterial communities of a melting High Arctic glacier snowpack

PubMed Central

Hell, Katherina; Edwards, Arwyn; Zarsky, Jakub; Podmirseg, Sabine M; Girdwood, Susan; Pachebat, Justin A; Insam, Heribert; Sattler, Birgit

2013-01-01

Snow environments can occupy over a third of land surface area, but little is known about the dynamics of snowpack bacteria. The effect of snow melt on bacterial community structure and diversity of surface environments of a Svalbard glacier was examined using analyses of 16S rRNA genes via T-RFLP, qPCR and 454 pyrosequencing. Distinct community structures were found in different habitat types, with changes over 1 week apparent, in particular for the dominant bacterial class present, Betaproteobacteria. The differences observed were consistent with influences from depositional mode (snowfall vs aeolian dusts), contrasting snow with dust-rich snow layers and near-surface ice. Contrary to that, slush as the decompositional product of snow harboured distinct lineages of bacteria, further implying post-depositional changes in community structure. Taxa affiliated to the betaproteobacterial genus Polaromonas were particularly dynamic, and evidence for the presence of betaproteobacterial ammonia-oxidizing bacteria was uncovered, inviting the prospect that the dynamic bacterial communities associated with snowpacks may be active in supraglacial nitrogen cycling and capable of rapid responses to changes induced by snowmelt. Furthermore the potential of supraglacial snowpack ecosystems to respond to transient yet spatially extensive melting episodes such as that observed across most of Greenland's ice sheet in 2012 merits further investigation. PMID:23552623

The dynamic bacterial communities of a melting High Arctic glacier snowpack.

PubMed

Hell, Katherina; Edwards, Arwyn; Zarsky, Jakub; Podmirseg, Sabine M; Girdwood, Susan; Pachebat, Justin A; Insam, Heribert; Sattler, Birgit

2013-09-01

Snow environments can occupy over a third of land surface area, but little is known about the dynamics of snowpack bacteria. The effect of snow melt on bacterial community structure and diversity of surface environments of a Svalbard glacier was examined using analyses of 16S rRNA genes via T-RFLP, qPCR and 454 pyrosequencing. Distinct community structures were found in different habitat types, with changes over 1 week apparent, in particular for the dominant bacterial class present, Betaproteobacteria. The differences observed were consistent with influences from depositional mode (snowfall vs aeolian dusts), contrasting snow with dust-rich snow layers and near-surface ice. Contrary to that, slush as the decompositional product of snow harboured distinct lineages of bacteria, further implying post-depositional changes in community structure. Taxa affiliated to the betaproteobacterial genus Polaromonas were particularly dynamic, and evidence for the presence of betaproteobacterial ammonia-oxidizing bacteria was uncovered, inviting the prospect that the dynamic bacterial communities associated with snowpacks may be active in supraglacial nitrogen cycling and capable of rapid responses to changes induced by snowmelt. Furthermore the potential of supraglacial snowpack ecosystems to respond to transient yet spatially extensive melting episodes such as that observed across most of Greenland's ice sheet in 2012 merits further investigation.

Management intensity alters decomposition via biological pathways

USGS Publications Warehouse

Wickings, Kyle; Grandy, A. Stuart; Reed, Sasha; Cleveland, Cory

2011-01-01

Current conceptual models predict that changes in plant litter chemistry during decomposition are primarily regulated by both initial litter chemistry and the stage-or extent-of mass loss. Far less is known about how variations in decomposer community structure (e.g., resulting from different ecosystem management types) could influence litter chemistry during decomposition. Given the recent agricultural intensification occurring globally and the importance of litter chemistry in regulating soil organic matter storage, our objectives were to determine the potential effects of agricultural management on plant litter chemistry and decomposition rates, and to investigate possible links between ecosystem management, litter chemistry and decomposition, and decomposer community composition and activity. We measured decomposition rates, changes in litter chemistry, extracellular enzyme activity, microarthropod communities, and bacterial versus fungal relative abundance in replicated conventional-till, no-till, and old field agricultural sites for both corn and grass litter. After one growing season, litter decomposition under conventional-till was 20% greater than in old field communities. However, decomposition rates in no-till were not significantly different from those in old field or conventional-till sites. After decomposition, grass residue in both conventional- and no-till systems was enriched in total polysaccharides relative to initial litter, while grass litter decomposed in old fields was enriched in nitrogen-bearing compounds and lipids. These differences corresponded with differences in decomposer communities, which also exhibited strong responses to both litter and management type. Overall, our results indicate that agricultural intensification can increase litter decomposition rates, alter decomposer communities, and influence litter chemistry in ways that could have important and long-term effects on soil organic matter dynamics. We suggest that future efforts to more accurately predict soil carbon dynamics under different management regimes may need to explicitly consider how changes in litter chemistry during decomposition are influenced by the specific metabolic capabilities of the extant decomposer communities.

Litter composition effects on decomposition across the litter-soil interface

EPA Science Inventory

Background/Question/Methods Many studies have investigated the influence of plant litter species composition on decomposition dynamics, but given the variety of communities and environments around the world, a variety of consequences of litter-mixing have been reported. Litter ...

Vibration energy harvesting based monitoring of an operational bridge undergoing forced vibration and train passage

NASA Astrophysics Data System (ADS)

Cahill, Paul; Hazra, Budhaditya; Karoumi, Raid; Mathewson, Alan; Pakrashi, Vikram

2018-06-01

The application of energy harvesting technology for monitoring civil infrastructure is a bourgeoning topic of interest. The ability of kinetic energy harvesters to scavenge ambient vibration energy can be useful for large civil infrastructure under operational conditions, particularly for bridge structures. The experimental integration of such harvesters with full scale structures and the subsequent use of the harvested energy directly for the purposes of structural health monitoring shows promise. This paper presents the first experimental deployment of piezoelectric vibration energy harvesting devices for monitoring a full-scale bridge undergoing forced dynamic vibrations under operational conditions using energy harvesting signatures against time. The calibration of the harvesters is presented, along with details of the host bridge structure and the dynamic assessment procedures. The measured responses of the harvesters from the tests are presented and the use the harvesters for the purposes of structural health monitoring (SHM) is investigated using empirical mode decomposition analysis, following a bespoke data cleaning approach. Finally, the use of sequential Karhunen Loeve transforms to detect train passages during the dynamic assessment is presented. This study is expected to further develop interest in energy-harvesting based monitoring of large infrastructure for both research and commercial purposes.

Computationally efficient methods for modelling laser wakefield acceleration in the blowout regime

NASA Astrophysics Data System (ADS)

Cowan, B. M.; Kalmykov, S. Y.; Beck, A.; Davoine, X.; Bunkers, K.; Lifschitz, A. F.; Lefebvre, E.; Bruhwiler, D. L.; Shadwick, B. A.; Umstadter, D. P.; Umstadter

2012-08-01

Electron self-injection and acceleration until dephasing in the blowout regime is studied for a set of initial conditions typical of recent experiments with 100-terawatt-class lasers. Two different approaches to computationally efficient, fully explicit, 3D particle-in-cell modelling are examined. First, the Cartesian code vorpal (Nieter, C. and Cary, J. R. 2004 VORPAL: a versatile plasma simulation code. J. Comput. Phys. 196, 538) using a perfect-dispersion electromagnetic solver precisely describes the laser pulse and bubble dynamics, taking advantage of coarser resolution in the propagation direction, with a proportionally larger time step. Using third-order splines for macroparticles helps suppress the sampling noise while keeping the usage of computational resources modest. The second way to reduce the simulation load is using reduced-geometry codes. In our case, the quasi-cylindrical code calder-circ (Lifschitz, A. F. et al. 2009 Particle-in-cell modelling of laser-plasma interaction using Fourier decomposition. J. Comput. Phys. 228(5), 1803-1814) uses decomposition of fields and currents into a set of poloidal modes, while the macroparticles move in the Cartesian 3D space. Cylindrical symmetry of the interaction allows using just two modes, reducing the computational load to roughly that of a planar Cartesian simulation while preserving the 3D nature of the interaction. This significant economy of resources allows using fine resolution in the direction of propagation and a small time step, making numerical dispersion vanishingly small, together with a large number of particles per cell, enabling good particle statistics. Quantitative agreement of two simulations indicates that these are free of numerical artefacts. Both approaches thus retrieve the physically correct evolution of the plasma bubble, recovering the intrinsic connection of electron self-injection to the nonlinear optical evolution of the driver.

The effects of temperature on decomposition and allelopathic phytotoxicity of boneseed litter.

PubMed

Al Harun, Md Abdullah Yousuf; Johnson, Joshua; Uddin, Md Nazim; Robinson, Randall W

2015-07-01

Decomposition of plant litter is a fundamental process in ecosystem function, carbon and nutrient cycling and, by extension, climate change. This study aimed to investigate the role of temperature on the decomposition of water soluble phenolics (WSP), carbon and soil nutrients in conjunction with the phytotoxicity dynamics of Chrysanthemoides monilifera subsp. monilifera (boneseed) litter. Treatments consisted of three factors including decomposition materials (litter alone, litter with soil and soil alone), decomposition periods and temperatures (5-15, 15-25 and 25-35°C (night/day)). Leachates were collected on 0, 5, 10, 20, 40 and 60th days to analyse physico-chemical parameters and phytotoxicity. Water soluble phenolics and dissolved organic carbon (DOC) increased with increasing temperature while nutrients like SO4(-2) and NO3(-1) decreased. Speed of germination, hypocotyl and radical length and weight of Lactuca sativa exposed to leachates were decreased with increasing decomposition temperature. All treatment components had significant effects on these parameters. There had a strong correlation between DOC and WSP, and WSP content of the leachates with radical length of test species. This study identified complex interactivity among temperature, WSP, DOC and soil nutrient dynamics of litter occupied soil and that these factors work together to influence phytotoxicity. Copyright © 2015. Published by Elsevier B.V.

A Novel Approach to Resonant Absorption of the Fast Magnetohydrodynamic Eigenmodes of a Coronal Arcade

NASA Astrophysics Data System (ADS)

Hindman, Bradley W.; Jain, Rekha

2018-05-01

The arched field lines forming coronal arcades are often observed to undulate as magnetohydrodynamic waves propagate both across and along the magnetic field. These waves are most likely a combination of resonantly coupled fast magnetoacoustic waves and Alfvén waves. The coupling results in resonant absorption of the fast waves, converting fast wave energy into Alfvén waves. The fast eigenmodes of the arcade have proven difficult to compute or derive analytically, largely because of the mathematical complexity that the coupling introduces. When a traditional spectral decomposition is employed, the discrete spectrum associated with the fast eigenmodes is often subsumed into the continuous Alfvén spectrum. Thus fast eigenmodes become collective modes or quasi-modes. Here we present a spectral decomposition that treats the eigenmodes as having real frequencies but complex wavenumbers. Using this procedure we derive dispersion relations, spatial damping rates, and eigenfunctions for the resonant, fast eigenmodes of the arcade. We demonstrate that resonant absorption introduces a fast mode that would not exist otherwise. This new mode is heavily damped by resonant absorption, travelling only a few wavelengths before losing most of its energy.

Variational mode decomposition based approach for accurate classification of color fundus images with hemorrhages

NASA Astrophysics Data System (ADS)

Lahmiri, Salim; Shmuel, Amir

2017-11-01

Diabetic retinopathy is a disease that can cause a loss of vision. An early and accurate diagnosis helps to improve treatment of the disease and prognosis. One of the earliest characteristics of diabetic retinopathy is the appearance of retinal hemorrhages. The purpose of this study is to design a fully automated system for the detection of hemorrhages in a retinal image. In the first stage of our proposed system, a retinal image is processed with variational mode decomposition (VMD) to obtain the first variational mode, which captures the high frequency components of the original image. In the second stage, four texture descriptors are extracted from the first variational mode. Finally, a classifier trained with all computed texture descriptors is used to distinguish between images of healthy and unhealthy retinas with hemorrhages. Experimental results showed evidence of the effectiveness of the proposed system for detection of hemorrhages in the retina, since a perfect detection rate was achieved. Our proposed system for detecting diabetic retinopathy is simple and easy to implement. It requires only short processing time, and it yields higher accuracy in comparison with previously proposed methods for detecting diabetic retinopathy.

Fault detection, isolation, and diagnosis of self-validating multifunctional sensors.

PubMed

Yang, Jing-Li; Chen, Yin-Sheng; Zhang, Li-Li; Sun, Zhen

2016-06-01

A novel fault detection, isolation, and diagnosis (FDID) strategy for self-validating multifunctional sensors is presented in this paper. The sparse non-negative matrix factorization-based method can effectively detect faults by using the squared prediction error (SPE) statistic, and the variables contribution plots based on SPE statistic can help to locate and isolate the faulty sensitive units. The complete ensemble empirical mode decomposition is employed to decompose the fault signals to a series of intrinsic mode functions (IMFs) and a residual. The sample entropy (SampEn)-weighted energy values of each IMFs and the residual are estimated to represent the characteristics of the fault signals. Multi-class support vector machine is introduced to identify the fault mode with the purpose of diagnosing status of the faulty sensitive units. The performance of the proposed strategy is compared with other fault detection strategies such as principal component analysis, independent component analysis, and fault diagnosis strategies such as empirical mode decomposition coupled with support vector machine. The proposed strategy is fully evaluated in a real self-validating multifunctional sensors experimental system, and the experimental results demonstrate that the proposed strategy provides an excellent solution to the FDID research topic of self-validating multifunctional sensors.

Implementation of the force decomposition machine for molecular dynamics simulations.

PubMed

Borštnik, Urban; Miller, Benjamin T; Brooks, Bernard R; Janežič, Dušanka

2012-09-01

We present the design and implementation of the force decomposition machine (FDM), a cluster of personal computers (PCs) that is tailored to running molecular dynamics (MD) simulations using the distributed diagonal force decomposition (DDFD) parallelization method. The cluster interconnect architecture is optimized for the communication pattern of the DDFD method. Our implementation of the FDM relies on standard commodity components even for networking. Although the cluster is meant for DDFD MD simulations, it remains general enough for other parallel computations. An analysis of several MD simulation runs on both the FDM and a standard PC cluster demonstrates that the FDM's interconnect architecture provides a greater performance compared to a more general cluster interconnect. Copyright © 2012 Elsevier Inc. All rights reserved.

Ab initio molecular dynamics study on the initial chemical events in nitramines: thermal decomposition of CL-20.

PubMed

Isayev, Olexandr; Gorb, Leonid; Qasim, Mo; Leszczynski, Jerzy

2008-09-04

CL-20 (2,4,6,8,10,12-hexanitro-2,4,6,8,10,12-hexaazaisowurtzitane or HNIW) is a high-energy nitramine explosive. To improve atomistic understanding of the thermal decomposition of CL-20 gas and solid phases, we performed a series of ab initio molecular dynamics simulations. We found that during unimolecular decomposition, unlike other nitramines (e.g., RDX, HMX), CL-20 has only one distinct initial reaction channelhomolysis of the N-NO2 bond. We did not observe any HONO elimination reaction during unimolecular decomposition, whereas the ring-breaking reaction was followed by NO 2 fission. Therefore, in spite of limited sampling, that provides a mostly qualitative picture, we proposed here a scheme of unimolecular decomposition of CL-20. The averaged product population over all trajectories was estimated at four HCN, two to four NO2, two to four NO, one CO, and one OH molecule per one CL-20 molecule. Our simulations provide a detailed description of the chemical processes in the initial stages of thermal decomposition of condensed CL-20, allowing elucidation of key features of such processes as composition of primary reaction products, reaction timing, and Arrhenius behavior of the system. The primary reactions leading to NO2, NO, N 2O, and N2 occur at very early stages. We also estimated potential activation barriers for the formation of NO2, which essentially determines overall decomposition kinetics and effective rate constants for NO2 and N2. The calculated solid-phase decomposition pathways correlate with available condensed-phase experimental data.

A molecular dynamics study of model SI clathrate hydrates: the effect of guest size and guest-water interaction on decomposition kinetics.

PubMed

Das, Subhadip; Baghel, Vikesh Singh; Roy, Sudip; Kumar, Rajnish

2015-04-14

One of the options suggested for methane recovery from natural gas hydrates is molecular replacement of methane by suitable guests like CO2 and N2. This approach has been found to be feasible through many experimental and molecular dynamics simulation studies. However, the long term stability of the resultant hydrate needs to be evaluated; the decomposition rate of these hydrates is expected to depend on the interaction between these guest and water molecules. In this work, molecular dynamics simulation has been performed to illustrate the effect of guest molecules with different sizes and interaction strengths with water on structure I (SI) hydrate decomposition and hence the stability. The van der Waals interaction between water of hydrate cages and guest molecules is defined by Lennard Jones potential parameters. A wide range of parameter spaces has been scanned by changing the guest molecules in the SI hydrate, which acts as a model gas for occupying the small and large cages of the SI hydrate. All atomistic simulation results show that the stability of the hydrate is sensitive to the size and interaction of the guest molecules with hydrate water. The increase in the interaction of guest molecules with water stabilizes the hydrate, which in turn shows a slower rate of hydrate decomposition. Similarly guest molecules with a reasonably small (similar to Helium) or large size increase the decomposition rate. The results were also analyzed by calculating the structural order parameter to understand the dynamics of crystal structure and correlated with the release rate of guest molecules from the solid hydrate phase. The results have been explained based on the calculation of potential energies felt by guest molecules in amorphous water, hydrate bulk and hydrate-water interface regions.

Optical diagnosis of cervical cancer by intrinsic mode functions

NASA Astrophysics Data System (ADS)

Mukhopadhyay, Sabyasachi; Pratiher, Sawon; Pratiher, Souvik; Pradhan, Asima; Ghosh, Nirmalya; Panigrahi, Prasanta K.

2017-03-01

In this paper, we make use of the empirical mode decomposition (EMD) to discriminate the cervical cancer tissues from normal ones based on elastic scattering spectroscopy. The phase space has been reconstructed through decomposing the optical signal into a finite set of bandlimited signals known as intrinsic mode functions (IMFs). It has been shown that the area measure of the analytic IMFs provides a good discrimination performance. Simulation results validate the efficacy of the IMFs followed by SVM based classification.

Raman intensity and vibrational modes of armchair CNTs

NASA Astrophysics Data System (ADS)

Hur, Jaewoong; Stuart, Steven J.

2017-07-01

Raman intensity changes and frequency patterns have been studied using the various armchair (n, n) to understand the variations of bond polarizability, in regard to changing diameters, lengths, and the number of atoms in the (n, n). The Raman intensity trends of the (n, n) are validated by those of Cn isomers. For frequency trends, similar frequency patterns and frequency inward shifts for the (n, n) are characterized. Also, VDOS trends of the (n, n) expressing Raman modes are interpreted. The decomposition of vibrational modes in the (n, n) into radial, longitudinal, and tangential mode is beneficially used to recognize the distinct characteristics of vibrational modes.

Multifractal Detrended Fluctuation Analysis of Regional Precipitation Sequences Based on the CEEMDAN-WPT

NASA Astrophysics Data System (ADS)

Liu, Dong; Cheng, Chen; Fu, Qiang; Liu, Chunlei; Li, Mo; Faiz, Muhammad Abrar; Li, Tianxiao; Khan, Muhammad Imran; Cui, Song

2018-03-01

In this paper, the complete ensemble empirical mode decomposition with the adaptive noise (CEEMDAN) algorithm is introduced into the complexity research of precipitation systems to improve the traditional complexity measure method specific to the mode mixing of the Empirical Mode Decomposition (EMD) and incomplete decomposition of the ensemble empirical mode decomposition (EEMD). We combined the CEEMDAN with the wavelet packet transform (WPT) and multifractal detrended fluctuation analysis (MF-DFA) to create the CEEMDAN-WPT-MFDFA, and used it to measure the complexity of the monthly precipitation sequence of 12 sub-regions in Harbin, Heilongjiang Province, China. The results show that there are significant differences in the monthly precipitation complexity of each sub-region in Harbin. The complexity of the northwest area of Harbin is the lowest and its predictability is the best. The complexity and predictability of the middle and Midwest areas of Harbin are about average. The complexity of the southeast area of Harbin is higher than that of the northwest, middle, and Midwest areas of Harbin and its predictability is worse. The complexity of Shuangcheng is the highest and its predictability is the worst of all the studied sub-regions. We used terrain and human activity as factors to analyze the causes of the complexity of the local precipitation. The results showed that the correlations between the precipitation complexity and terrain are obvious, and the correlations between the precipitation complexity and human influence factors vary. The distribution of the precipitation complexity in this area may be generated by the superposition effect of human activities and natural factors such as terrain, general atmospheric circulation, land and sea location, and ocean currents. To evaluate the stability of the algorithm, the CEEMDAN-WPT-MFDFA was compared with the equal probability coarse graining LZC algorithm, fuzzy entropy, and wavelet entropy. The results show that the CEEMDAN-WPT-MFDFA was more stable than 3 contrast methods under the influence of white noise and colored noise, which proves that the CEEMDAN-WPT-MFDFA has a strong robustness under the influence of noise.

Dynamics of entanglement and uncertainty relation in coupled harmonic oscillator system: exact results

NASA Astrophysics Data System (ADS)

Park, DaeKil

2018-06-01

The dynamics of entanglement and uncertainty relation is explored by solving the time-dependent Schrödinger equation for coupled harmonic oscillator system analytically when the angular frequencies and coupling constant are arbitrarily time dependent. We derive the spectral and Schmidt decompositions for vacuum solution. Using the decompositions, we derive the analytical expressions for von Neumann and Rényi entropies. Making use of Wigner distribution function defined in phase space, we derive the time dependence of position-momentum uncertainty relations. To show the dynamics of entanglement and uncertainty relation graphically, we introduce two toy models and one realistic quenched model. While the dynamics can be conjectured by simple consideration in the toy models, the dynamics in the realistic quenched model is somewhat different from that in the toy models. In particular, the dynamics of entanglement exhibits similar pattern to dynamics of uncertainty parameter in the realistic quenched model.

Self-force via m-mode regularization and 2+1D evolution: Foundations and a scalar-field implementation on Schwarzschild spacetime

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dolan, Sam R.; Barack, Leor

2011-01-15

To model the radiative evolution of extreme mass-ratio binary inspirals (a key target of the LISA mission), the community needs efficient methods for computation of the gravitational self-force (SF) on the Kerr spacetime. Here we further develop a practical 'm-mode regularization' scheme for SF calculations, and give the details of a first implementation. The key steps in the method are (i) removal of a singular part of the perturbation field with a suitable 'puncture' to leave a sufficiently regular residual within a finite worldtube surrounding the particle's worldline, (ii) decomposition in azimuthal (m) modes, (iii) numerical evolution of the mmore » modes in 2+1D with a finite-difference scheme, and (iv) reconstruction of the SF from the mode sum. The method relies on a judicious choice of puncture, based on the Detweiler-Whiting decomposition. We give a working definition for the ''order'' of the puncture, and show how it determines the convergence rate of the m-mode sum. The dissipative piece of the SF displays an exponentially convergent mode sum, while the m-mode sum for the conservative piece converges with a power law. In the latter case, the individual modal contributions fall off at large m as m{sup -n} for even n and as m{sup -n+1} for odd n, where n is the puncture order. We describe an m-mode implementation with a 4th-order puncture to compute the scalar-field SF along circular geodesics on Schwarzschild. In a forthcoming companion paper we extend the calculation to the Kerr spacetime.« less

Integrated aerodynamic/dynamic/structural optimization of helicopter rotor blades using multilevel decomposition

NASA Technical Reports Server (NTRS)

Walsh, Joanne L.; Young, Katherine C.; Pritchard, Jocelyn I.; Adelman, Howard M.; Mantay, Wayne R.

1995-01-01

This paper describes an integrated aerodynamic/dynamic/structural (IADS) optimization procedure for helicopter rotor blades. The procedure combines performance, dynamics, and structural analyses with a general-purpose optimizer using multilevel decomposition techniques. At the upper level, the structure is defined in terms of global quantities (stiffness, mass, and average strains). At the lower level, the structure is defined in terms of local quantities (detailed dimensions of the blade structure and stresses). The IADS procedure provides an optimization technique that is compatible with industrial design practices in which the aerodynamic and dynamic designs are performed at a global level and the structural design is carried out at a detailed level with considerable dialog and compromise among the aerodynamic, dynamic, and structural groups. The IADS procedure is demonstrated for several examples.

Multilevel decomposition approach to integrated aerodynamic/dynamic/structural optimization of helicopter rotor blades

NASA Technical Reports Server (NTRS)

Walsh, Joanne L.; Young, Katherine C.; Pritchard, Jocelyn I.; Adelman, Howard M.; Mantay, Wayne R.

1994-01-01

This paper describes an integrated aerodynamic, dynamic, and structural (IADS) optimization procedure for helicopter rotor blades. The procedure combines performance, dynamics, and structural analyses with a general purpose optimizer using multilevel decomposition techniques. At the upper level, the structure is defined in terms of local quantities (stiffnesses, mass, and average strains). At the lower level, the structure is defined in terms of local quantities (detailed dimensions of the blade structure and stresses). The IADS procedure provides an optimization technique that is compatible with industrial design practices in which the aerodynamic and dynamic design is performed at a global level and the structural design is carried out at a detailed level with considerable dialogue and compromise among the aerodynamic, dynamic, and structural groups. The IADS procedure is demonstrated for several cases.

Evolution of the bi-stable wake of a square-back automotive shape

NASA Astrophysics Data System (ADS)

Pavia, Giancarlo; Passmore, Martin; Sardu, Costantino

2018-01-01

Square-back shapes are popular in the automotive market for their high level of practicality. These geometries, however, are usually characterised by high drag and their wake dynamics present aspects, such as the coexistence of a long-time bi-stable behaviour and short-time global fluctuating modes that are not fully understood. In the present paper, the unsteady behaviour of the wake of a generic square-back car geometry is characterised with an emphasis on identifying the causal relationship between the different dynamic modes in the wake. The study is experimental, consisting of balance, pressure, and stereoscopic PIV measurements. Applying wavelet and cross-wavelet transforms to the balance data, a quasi-steady correlation is demonstrated between the forces and bi-stable modes. This is investigated by applying proper orthogonal decomposition to the pressure and velocity data sets and a new structure is proposed for each bi-stable state, consisting of a hairpin vortex that originates from one of the two model's vertical trailing edges and bends towards the opposite side as it merges into a single streamwise vortex downstream. The wake pumping motion is also identified and for the first time linked with the motion of the bi-stable vortical structure in the streamwise direction, resulting in out-of-phase pressure variations between the two vertical halves of the model base. A phase-averaged low-order model is also proposed that provides a comprehensive description of the mechanisms of the switch between the bi-stable states. It is demonstrated that, during the switch, the wake becomes laterally symmetric and, at this point, the level of interaction between the recirculating structures and the base reaches a minimum, yielding, for this geometry, a 7% reduction of the base drag compared to the time-averaged result.

THE DYNAMICS OF THE SOLAR MAGNETIC FIELD: POLARITY REVERSALS, BUTTERFLY DIAGRAM, AND QUASI-BIENNIAL OSCILLATIONS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vecchio, A.; Meduri, D.; Carbone, V.

2012-04-10

The spatio-temporal dynamics of the solar magnetic field has been investigated by using NSO/Kitt Peak magnetic synoptic maps covering the period 1976 August-2003 September. The field radial component, for each heliographic latitude, has been decomposed in intrinsic mode functions through the Empirical Mode Decomposition in order to investigate the time evolution of the various characteristic oscillating modes at different latitudes. The same technique has also been applied on synoptic maps of the meridional and east-west components, which were derived from the observed line-of-sight projection of the field by using the differential rotation. Results obtained for the {approx}22 yr cycle, relatedmore » to the polarity inversions of the large-scale dipolar field, show an antisymmetric behavior with respect to the equator in all the field components and a marked poleward flux migration in the radial and meridional components (from about -35 Degree-Sign and +35 Degree-Sign in the southern and northern hemispheres, respectively). The quasi-biennial oscillations (QBOs) are also identified as a fundamental timescale of variability of the magnetic field and associated with poleward magnetic flux migration from low latitudes around the maximum and descending phase of the solar cycle. Moreover, signs of an equatorward drift, at a {approx}2 yr rate, seem to appear in the radial and toroidal components. Hence, the QBO patterns suggest a link to a dynamo action. Finally, the high-frequency component of the magnetic field, at timescales less than 1 yr, provides the most energetic contribution and it is associated with the outbreaks of the bipolar regions on the solar surface.« less

How does low temperature coupled with different pressures affect initiation mechanisms and subsequent decompositions in nitramine explosive HMX?

PubMed

Wu, Qiong; Xiong, Guolin; Zhu, Weihua; Xiao, Heming

2015-09-21

We have performed ab initio molecular dynamics simulations to study coupling effects of temperature (534-873 K) and pressure (1-20 GPa) on the initiation mechanisms and subsequent chemical decompositions of nitramine explosive 1,3,5,7-tetranitro-1,3,5,7-tetrazocane (HMX). A new initiation decomposition mechanism of HMX was found to be the unimolecular C-H bond breaking, and this mechanism was independent of the coupling effects of different temperatures and pressures. The formed hydrogen radicals could promote subsequent decompositions of HMX. Subsequent decompositions were very sensitive to the pressure at low temperatures (534 and 608 K), while the temperature became the foremost factor that affected the decomposition at a high temperature (873 K) instead of the pressure. Our study may provide a new insight into understanding the coupling effects of the temperature and pressure on the initiation decomposition mechanisms of nitramine explosives.

Early-stage changes in natural (13)C and (15)N abundance and nutrient dynamics during different litter decomposition.

PubMed

Gautam, Mukesh Kumar; Lee, Kwang-Sik; Song, Byeong-Yeol; Lee, Dongho; Bong, Yeon-Sik

2016-05-01

Decomposition, nutrient, and isotopic (δ(13)C and δ(15)N) dynamics during 1 year were studied for leaf and twig litters of Pinus densiflora, Castanea crenata, Erigeron annuus, and Miscanthus sinensis growing on a highly weathered soil with constrained nutrient supply using litterbags in a cool temperate region of South Korea. Decay constant (k/year) ranged from 0.58 to 1.29/year, and mass loss ranged from 22.36 to 58.43 % among litter types. The results demonstrate that mass loss and nutrient dynamics of decomposing litter were influenced by the seasonality of mineralization and immobilization processes. In general, most nutrients exhibited alternate phases of rapid mineralization followed by gradual immobilization, except K, which was released throughout the field incubation. At the end of study, among all the nutrients only N and P showed net immobilization. Mobility of different nutrients from decomposing litter as the percentage of initial litter nutrient concentration was in the order of K > Mg > Ca > N ≈ P. The δ(13)C (0.32-6.70 ‰) and δ(15)N (0.74-3.90 ‰) values of residual litters showed nonlinear increase and decrease, respectively compared to initial isotopic values during decomposition. Litter of different functional types and chemical quality converged toward a conservative nutrient use strategy through mechanisms of slow decomposition and slow nutrient mobilization. Our results indicate that litter quality and season, are the most important regulators of litter decomposition in these forests. The results revealed significant relationships between litter decomposition rates and N, C:N ratio and P, and seasonality (temperature). These results and the convergence of different litters towards conservative nutrient use in these nutrient constrained ecosystems imply optimization of litter management because litter removal can have cascading effects on litter decomposition and nutrient availability in these systems.

An improved algorithm for balanced POD through an analytic treatment of impulse response tails

NASA Astrophysics Data System (ADS)

Tu, Jonathan H.; Rowley, Clarence W.

2012-06-01

We present a modification of the balanced proper orthogonal decomposition (balanced POD) algorithm for systems with simple impulse response tails. In this new method, we use dynamic mode decomposition (DMD) to estimate the slowly decaying eigenvectors that dominate the long-time behavior of the direct and adjoint impulse responses. This is done using a new, low-memory variant of the DMD algorithm, appropriate for large datasets. We then formulate analytic expressions for the contribution of these eigenvectors to the controllability and observability Gramians. These contributions can be accounted for in the balanced POD algorithm by simply appending the impulse response snapshot matrices (direct and adjoint, respectively) with particular linear combinations of the slow eigenvectors. Aside from these additions to the snapshot matrices, the algorithm remains unchanged. By treating the tails analytically, we eliminate the need to run long impulse response simulations, lowering storage requirements and speeding up ensuing computations. To demonstrate its effectiveness, we apply this method to two examples: the linearized, complex Ginzburg-Landau equation, and the two-dimensional fluid flow past a cylinder. As expected, reduced-order models computed using an analytic tail match or exceed the accuracy of those computed using the standard balanced POD procedure, at a fraction of the cost.

A dynamic model of a cantilever beam with a closed, embedded horizontal crack including local flexibilities at crack tips

NASA Astrophysics Data System (ADS)

Liu, J.; Zhu, W. D.; Charalambides, P. G.; Shao, Y. M.; Xu, Y. F.; Fang, X. M.

2016-11-01

As one of major failure modes of mechanical structures subjected to periodic loads, embedded cracks due to fatigue can cause catastrophic failure of machineries. Understanding the dynamic characteristics of a structure with an embedded crack is helpful for early crack detection and diagnosis. In this work, a new three-segment beam model with local flexibilities at crack tips is developed to investigate the vibration of a cantilever beam with a closed, fully embedded horizontal crack, which is assumed to be not located at its clamped or free end or distributed near its top or bottom side. The three-segment beam model is assumed to be a linear elastic system, and it does not account for the nonlinear crack closure effect; the top and bottom segments always stay in contact at their interface during the beam vibration. It can model the effects of local deformations in the vicinity of the crack tips, which cannot be captured by previous methods in the literature. The middle segment of the beam containing the crack is modeled by a mechanically consistent, reduced bending moment. Each beam segment is assumed to be an Euler-Bernoulli beam, and the compliances at the crack tips are analytically determined using a J-integral approach and verified using commercial finite element software. Using compatibility conditions at the crack tips and the transfer matrix method, the nature frequencies and mode shapes of the cracked cantilever beam are obtained. The three-segment beam model is used to investigate the effects of local flexibilities at crack tips on the first three natural frequencies and mode shapes of the cracked cantilever beam. A stationary wavelet transform (SWT) method is used to process the mode shapes of the cracked cantilever beam; jumps in single-level SWT decomposition detail coefficients can be used to identify the length and location of an embedded horizontal crack.

Chronic Nitrogen Deposition Influences the Chemical Dynamics of Leaf Litter and Fine Roots During Decomposition

EPA Science Inventory

Atmospheric nitrogen deposition induces a forest carbon sink across broad parts of the Northern Hemisphere; this carbon sink may partly result from slower litter decomposition. Although microbial responses to experimental nitrogen deposition have been well-studied, evidence linki...

Reactive molecular dynamics simulation of solid nitromethane impact on (010) surfaces induced and nonimpact thermal decomposition.

PubMed

Guo, Feng; Cheng, Xin-lu; Zhang, Hong

2012-04-12

Which is the first step in the decomposition process of nitromethane is a controversial issue, proton dissociation or C-N bond scission. We applied reactive force field (ReaxFF) molecular dynamics to probe the initial decomposition mechanisms of nitromethane. By comparing the impact on (010) surfaces and without impact (only heating) for nitromethane simulations, we found that proton dissociation is the first step of the pyrolysis of nitromethane, and the C-N bond decomposes in the same time scale as in impact simulations, but in the nonimpact simulation, C-N bond dissociation takes place at a later time. At the end of these simulations, a large number of clusters are formed. By analyzing the trajectories, we discussed the role of the hydrogen bond in the initial process of nitromethane decompositions, the intermediates observed in the early time of the simulations, and the formation of clusters that consisted of C-N-C-N chain/ring structures.

Inviscid criterion for decomposing scales

NASA Astrophysics Data System (ADS)

Zhao, Dongxiao; Aluie, Hussein

2018-05-01

The proper scale decomposition in flows with significant density variations is not as straightforward as in incompressible flows, with many possible ways to define a "length scale." A choice can be made according to the so-called inviscid criterion [Aluie, Physica D 24, 54 (2013), 10.1016/j.physd.2012.12.009]. It is a kinematic requirement that a scale decomposition yield negligible viscous effects at large enough length scales. It has been proved [Aluie, Physica D 24, 54 (2013), 10.1016/j.physd.2012.12.009] recently that a Favre decomposition satisfies the inviscid criterion, which is necessary to unravel inertial-range dynamics and the cascade. Here we present numerical demonstrations of those results. We also show that two other commonly used decompositions can violate the inviscid criterion and, therefore, are not suitable to study inertial-range dynamics in variable-density and compressible turbulence. Our results have practical modeling implication in showing that viscous terms in Large Eddy Simulations do not need to be modeled and can be neglected.

Transition from Direct to Inverse Cascade in Three-Dimensional Turbulence

NASA Astrophysics Data System (ADS)

Sahoo, Ganapati; Alexakis, Alexandros; Biferale, Luca

2017-11-01

We study a model system where the triadic interactions in Navier-Stokes equations are enhanced or suppressed in a controlled manner without affecting neither the total number of degrees of freedom nor the ideal invariants and without breaking any of the symmetries of original equations. Our numerical simulations are based on the helical decomposition of velocity Fourier modes. We introduced a parameter (0 <= λ <= 1) that controls the relative weight among homochiral and heterochiral triads in the nonlinear evolution. We show that by using this weighting protocol the turbulent evolution displays a sharp transition, for a critical value of the control parameter, from forward to backward energy transfer but still keeping the dynamics fully three dimensional, isotropic, and parity invariant. AtMath Collaboration of University of Helsinki and ERC Grant No. 339032 `NewTurb'.

Performance of flapping airfoil propulsion with LBM method and DMD analysis

NASA Astrophysics Data System (ADS)

Li, Bing-Hua; Huang, Xian-Wen; Zheng, Yao; Xie, Fang-Fang; Wang, Jing; Zou, Jian-Feng

2018-05-01

In this work, the performance of flapping airfoil propulsion at low Reynolds number of Re = 100-400 is studied numerically with the lattice Boltzmann method (LBM). Combined with immersed boundary method (IBM), the LBM has been widely used to simulate moving boundary problems. The influences of the reduced frequency on the plunging and pitching airfoil are explored. It is found that the leading-edge vertex separation and inverted wake structures are two main coherent structures, which dominate the flapping airfoil propulsion. However, the two structures play different roles in the flow and the combination effects on the propulsion need to be clarified. To do so, we adopt the dynamic mode decomposition (DMD) algorithm to reveal the underlying physics. The DMD has been proven to be very suitable for analyzing the complex transient systems like the vortex structure of flapping flight.

Detecting and characterizing high-frequency oscillations in epilepsy: a case study of big data analysis

NASA Astrophysics Data System (ADS)

Huang, Liang; Ni, Xuan; Ditto, William L.; Spano, Mark; Carney, Paul R.; Lai, Ying-Cheng

2017-01-01

We develop a framework to uncover and analyse dynamical anomalies from massive, nonlinear and non-stationary time series data. The framework consists of three steps: preprocessing of massive datasets to eliminate erroneous data segments, application of the empirical mode decomposition and Hilbert transform paradigm to obtain the fundamental components embedded in the time series at distinct time scales, and statistical/scaling analysis of the components. As a case study, we apply our framework to detecting and characterizing high-frequency oscillations (HFOs) from a big database of rat electroencephalogram recordings. We find a striking phenomenon: HFOs exhibit on-off intermittency that can be quantified by algebraic scaling laws. Our framework can be generalized to big data-related problems in other fields such as large-scale sensor data and seismic data analysis.

Dynamical eigenfunction decomposition of turbulent channel flow

NASA Technical Reports Server (NTRS)

Ball, K. S.; Sirovich, L.; Keefe, L. R.

1991-01-01

The results of an analysis of low-Reynolds-number turbulent channel flow based on the Karhunen-Loeve (K-L) expansion are presented. The turbulent flow field is generated by a direct numerical simulation of the Navier-Stokes equations at a Reynolds number Re(tau) = 80 (based on the wall shear velocity and channel half-width). The K-L procedure is then applied to determine the eigenvalues and eigenfunctions for this flow. The random coefficients of the K-L expansion are subsequently found by projecting the numerical flow field onto these eigenfunctions. The resulting expansion captures 90 percent of the turbulent energy with significantly fewer modes than the original trigonometric expansion. The eigenfunctions, which appear either as rolls or shearing motions, possess viscous boundary layers at the walls and are much richer in harmonics than the original basis functions.

Volatility behavior of visibility graph EMD financial time series from Ising interacting system

NASA Astrophysics Data System (ADS)

Zhang, Bo; Wang, Jun; Fang, Wen

2015-08-01

A financial market dynamics model is developed and investigated by stochastic Ising system, where the Ising model is the most popular ferromagnetic model in statistical physics systems. Applying two graph based analysis and multiscale entropy method, we investigate and compare the statistical volatility behavior of return time series and the corresponding IMF series derived from the empirical mode decomposition (EMD) method. And the real stock market indices are considered to be comparatively studied with the simulation data of the proposed model. Further, we find that the degree distribution of visibility graph for the simulation series has the power law tails, and the assortative network exhibits the mixing pattern property. All these features are in agreement with the real market data, the research confirms that the financial model established by the Ising system is reasonable.

Stability evaluation of short-circuiting gas metal arc welding based on ensemble empirical mode decomposition

NASA Astrophysics Data System (ADS)

Huang, Yong; Wang, Kehong; Zhou, Zhilan; Zhou, Xiaoxiao; Fang, Jimi

2017-03-01

The arc of gas metal arc welding (GMAW) contains abundant information about its stability and droplet transition, which can be effectively characterized by extracting the arc electrical signals. In this study, ensemble empirical mode decomposition (EEMD) was used to evaluate the stability of electrical current signals. The welding electrical signals were first decomposed by EEMD, and then transformed to a Hilbert-Huang spectrum and a marginal spectrum. The marginal spectrum is an approximate distribution of amplitude with frequency of signals, and can be described by a marginal index. Analysis of various welding process parameters showed that the marginal index of current signals increased when the welding process was more stable, and vice versa. Thus EEMD combined with the marginal index can effectively uncover the stability and droplet transition of GMAW.

Phase transformations of siderite ore by the thermomagnetic analysis data

NASA Astrophysics Data System (ADS)

Ponomar, V. P.; Dudchenko, N. O.; Brik, A. B.

2017-02-01

Thermal decomposition of Bakal siderite ore (that consists of magnesium siderite and ankerite traces) was investigated by thermomagnetic analysis. Thermomagnetic analysis was carried-out using laboratory-built facility that allows automatic registration of sample magnetization with the temperature (heating/cooling rate was 65°/min, maximum temperature 650 °C) at low- and high-oxygen content. Curie temperature gradually decreases with each next cycles of heating/cooling at low-oxygen content. Curie temperature decrease after 2nd cycle of heating/cooling at high-oxygen content and do not change with next cycles. Final Curie temperature for both modes was 320 °C. Saturation magnetization of obtained samples increases up to 20 Am2/kg. The final product of phase transformation at both modes was magnesioferrite. It was shown that intermediate phase of thermal decomposition of Bakal siderite ore was magnesiowustite.

Combined empirical mode decomposition and texture features for skin lesion classification using quadratic support vector machine.

PubMed

Wahba, Maram A; Ashour, Amira S; Napoleon, Sameh A; Abd Elnaby, Mustafa M; Guo, Yanhui

2017-12-01

Basal cell carcinoma is one of the most common malignant skin lesions. Automated lesion identification and classification using image processing techniques is highly required to reduce the diagnosis errors. In this study, a novel technique is applied to classify skin lesion images into two classes, namely the malignant Basal cell carcinoma and the benign nevus. A hybrid combination of bi-dimensional empirical mode decomposition and gray-level difference method features is proposed after hair removal. The combined features are further classified using quadratic support vector machine (Q-SVM). The proposed system has achieved outstanding performance of 100% accuracy, sensitivity and specificity compared to other support vector machine procedures as well as with different extracted features. Basal Cell Carcinoma is effectively classified using Q-SVM with the proposed combined features.

Adaptive multi-step Full Waveform Inversion based on Waveform Mode Decomposition

NASA Astrophysics Data System (ADS)

Hu, Yong; Han, Liguo; Xu, Zhuo; Zhang, Fengjiao; Zeng, Jingwen

2017-04-01

Full Waveform Inversion (FWI) can be used to build high resolution velocity models, but there are still many challenges in seismic field data processing. The most difficult problem is about how to recover long-wavelength components of subsurface velocity models when seismic data is lacking of low frequency information and without long-offsets. To solve this problem, we propose to use Waveform Mode Decomposition (WMD) method to reconstruct low frequency information for FWI to obtain a smooth model, so that the initial model dependence of FWI can be reduced. In this paper, we use adjoint-state method to calculate the gradient for Waveform Mode Decomposition Full Waveform Inversion (WMDFWI). Through the illustrative numerical examples, we proved that the low frequency which is reconstructed by WMD method is very reliable. WMDFWI in combination with the adaptive multi-step inversion strategy can obtain more faithful and accurate final inversion results. Numerical examples show that even if the initial velocity model is far from the true model and lacking of low frequency information, we still can obtain good inversion results with WMD method. From numerical examples of anti-noise test, we see that the adaptive multi-step inversion strategy for WMDFWI has strong ability to resist Gaussian noise. WMD method is promising to be able to implement for the land seismic FWI, because it can reconstruct the low frequency information, lower the dominant frequency in the adjoint source, and has a strong ability to resist noise.

Surface Chemistry of CWAs for Decon Enabling Sciences

DTIC Science & Technology

2014-11-04

representing the formation of a hydrogen-bonded mode. Characteristic modes of the sarin molecule itself are also observed. These experimental results show...Triangle Park, NC 27709-2211 surface science, CWA, uptake, decomposition, decontamination, filtration , XPS, FTIR, TPD, MS, UHV REPORT DOCUMENTATION PAGE 11...Karwacki, Team Leader CBR Filtration Research and Technology Directorate at ECBC. Through this collaboration, we have established a facility for the study

Optimal Multi-scale Demand-side Management for Continuous Power-Intensive Processes

NASA Astrophysics Data System (ADS)

Mitra, Sumit

With the advent of deregulation in electricity markets and an increasing share of intermittent power generation sources, the profitability of industrial consumers that operate power-intensive processes has become directly linked to the variability in energy prices. Thus, for industrial consumers that are able to adjust to the fluctuations, time-sensitive electricity prices (as part of so-called Demand-Side Management (DSM) in the smart grid) offer potential economical incentives. In this thesis, we introduce optimization models and decomposition strategies for the multi-scale Demand-Side Management of continuous power-intensive processes. On an operational level, we derive a mode formulation for scheduling under time-sensitive electricity prices. The formulation is applied to air separation plants and cement plants to minimize the operating cost. We also describe how a mode formulation can be used for industrial combined heat and power plants that are co-located at integrated chemical sites to increase operating profit by adjusting their steam and electricity production according to their inherent flexibility. Furthermore, a robust optimization formulation is developed to address the uncertainty in electricity prices by accounting for correlations and multiple ranges in the realization of the random variables. On a strategic level, we introduce a multi-scale model that provides an understanding of the value of flexibility of the current plant configuration and the value of additional flexibility in terms of retrofits for Demand-Side Management under product demand uncertainty. The integration of multiple time scales leads to large-scale two-stage stochastic programming problems, for which we need to apply decomposition strategies in order to obtain a good solution within a reasonable amount of time. Hence, we describe two decomposition schemes that can be applied to solve two-stage stochastic programming problems: First, a hybrid bi-level decomposition scheme with novel Lagrangean-type and subset-type cuts to strengthen the relaxation. Second, an enhanced cross-decomposition scheme that integrates Benders decomposition and Lagrangean decomposition on a scenario basis. To demonstrate the effectiveness of our developed methodology, we provide several industrial case studies throughout the thesis.

Excited electronic state decomposition mechanisms and dynamics of nitramine energetic materials and model systems

NASA Astrophysics Data System (ADS)

Greenfield, Margo

Energetic materials play an important role in aeronautics, the weapon industry, and the propellant industry due to their broad applications as explosives and fuels. RDX (1,3,5-trinitrohexahydro-s-triazine), HMX (octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine), and CL-20 (2,4,6,8,10,12-hexanitro-2,4,6,8,10,12-hexaazaisowurtzitane) are compounds which contain high energy density. Although RDX and HMX have been studied extensively over the past several decades a complete understanding of their decomposition mechanisms and dynamics is unknown. Time of flight mass spectroscopy (TOFMS) UV photodissociation (ns) experiments of gas phase RDX, HMX, and CL-20 generate the NO molecule as the initial decomposition product. Four different vibronic transitions of the initial decomposition product, the NO molecule, are observed: A2Sigma(upsilon'=0)←X 2pi(upsilon"=0,1,2,3). Simulations of the rovibronic intensities for the A←X transitions demonstrate that NO dissociated from RDX, HMX, and CL-20 is rotationally cold (˜20 K) and vibrationally hot (˜1800 K). Conversely, experiments on the five model systems (nitromethane, dimethylnitramine (DMNA), nitropyrrolidine, nitropiperidine and dinitropiperazine) produce rotationally hot and vibrationally cold spectra. Laser induced fluorescence (LIF) experiments are performed to rule out the possible decomposition product OH, generated along with NO, perhaps from the suggested HONO elimination mechanism. The OH radical is not observed in the fluorescence experiments, indicating the HONO decomposition intermediate is not an important pathway for the excited electronic state decomposition of cyclic nitramines. The NO molecule is also employed to measure the dynamics of the excited state decomposition. A 226 nm, 180 fs light pulse is utilized to photodissociate the gas phase systems. Stable ion states of DMNA and nitropyrrolidine are observed while the energetic materials and remaining model systems present the NO molecule as the only observed product. Pump-probe transients of the resonant A←X (0-0) transition of the NO molecule show a constant signal indicating these materials decompose faster than the time duration of the 226 nm laser light. Calculational results together with the experimental results indicate the energetic materials decompose through an internal conversion to very highly excited (˜5 eV of vibrational energy) vibrational states of their ground electronic state, while the model systems follow an excited electronic state decomposition pathway.

Fast flux module detection using matroid theory.

PubMed

Reimers, Arne C; Bruggeman, Frank J; Olivier, Brett G; Stougie, Leen

2015-05-01

Flux balance analysis (FBA) is one of the most often applied methods on genome-scale metabolic networks. Although FBA uniquely determines the optimal yield, the pathway that achieves this is usually not unique. The analysis of the optimal-yield flux space has been an open challenge. Flux variability analysis is only capturing some properties of the flux space, while elementary mode analysis is intractable due to the enormous number of elementary modes. However, it has been found by Kelk et al. (2012) that the space of optimal-yield fluxes decomposes into flux modules. These decompositions allow a much easier but still comprehensive analysis of the optimal-yield flux space. Using the mathematical definition of module introduced by Müller and Bockmayr (2013b), we discovered useful connections to matroid theory, through which efficient algorithms enable us to compute the decomposition into modules in a few seconds for genome-scale networks. Using that every module can be represented by one reaction that represents its function, in this article, we also present a method that uses this decomposition to visualize the interplay of modules. We expect the new method to replace flux variability analysis in the pipelines for metabolic networks.

Vibrational Dynamics and Guest-Host Coupling in Clathrate Hydrates

NASA Astrophysics Data System (ADS)

Koza, Michael M.; Schober, Helmut

Clathrate hydrates may turn out either a blessing or a curse for mankind. On one hand, they constitute a huge reservoir of fossil fuel. On the other hand, their decomposition may liberate large amounts of green house gas and have disastrous consequences on sea floor stability. It is thus of paramount importance to understand the formation and stability of these guest-host compounds. Neutron diffraction has successfully occupied a prominent place on the stage of these scientific investigations. Complete understanding, however, is not achieved without an explanation for the thermal properties of clathrates. In particular, the thermal conductivity has a large influence on clathrate formation and conservation. Neutron spectroscopy allows probing the microscopic dynamics of clathrate hydrates. We will show how comparative studies of vibrations in clathrate hydrates give insight into the coupling of the guest to the host lattice. This coupling together with the anharmonicity of the vibrational modes is shown to lay the foundations for the peculiar thermodynamic properties of clathrate hydrates. The results obtained reach far beyond the specific clathrate system. Similar mechanisms are expected to be at work in any guest-host complex.

Nonlinear dynamic analysis of cantilevered piezoelectric energy harvesters under simultaneous parametric and external excitations

NASA Astrophysics Data System (ADS)

Fang, Fei; Xia, Guanghui; Wang, Jianguo

2018-02-01

The nonlinear dynamics of cantilevered piezoelectric beams is investigated under simultaneous parametric and external excitations. The beam is composed of a substrate and two piezoelectric layers and assumed as an Euler-Bernoulli model with inextensible deformation. A nonlinear distributed parameter model of cantilevered piezoelectric energy harvesters is proposed using the generalized Hamilton's principle. The proposed model includes geometric and inertia nonlinearity, but neglects the material nonlinearity. Using the Galerkin decomposition method and harmonic balance method, analytical expressions of the frequency-response curves are presented when the first bending mode of the beam plays a dominant role. Using these expressions, we investigate the effects of the damping, load resistance, electromechanical coupling, and excitation amplitude on the frequency-response curves. We also study the difference between the nonlinear lumped-parameter and distributed-parameter model for predicting the performance of the energy harvesting system. Only in the case of parametric excitation, we demonstrate that the energy harvesting system has an initiation excitation threshold below which no energy can be harvested. We also illustrate that the damping and load resistance affect the initiation excitation threshold.

Nonlinear dynamic analysis of cantilevered piezoelectric energy harvesters under simultaneous parametric and external excitations

NASA Astrophysics Data System (ADS)

Fang, Fei; Xia, Guanghui; Wang, Jianguo

2018-06-01

The nonlinear dynamics of cantilevered piezoelectric beams is investigated under simultaneous parametric and external excitations. The beam is composed of a substrate and two piezoelectric layers and assumed as an Euler-Bernoulli model with inextensible deformation. A nonlinear distributed parameter model of cantilevered piezoelectric energy harvesters is proposed using the generalized Hamilton's principle. The proposed model includes geometric and inertia nonlinearity, but neglects the material nonlinearity. Using the Galerkin decomposition method and harmonic balance method, analytical expressions of the frequency-response curves are presented when the first bending mode of the beam plays a dominant role. Using these expressions, we investigate the effects of the damping, load resistance, electromechanical coupling, and excitation amplitude on the frequency-response curves. We also study the difference between the nonlinear lumped-parameter and distributed-parameter model for predicting the performance of the energy harvesting system. Only in the case of parametric excitation, we demonstrate that the energy harvesting system has an initiation excitation threshold below which no energy can be harvested. We also illustrate that the damping and load resistance affect the initiation excitation threshold.

Connectome-harmonic decomposition of human brain activity reveals dynamical repertoire re-organization under LSD.

PubMed

Atasoy, Selen; Roseman, Leor; Kaelen, Mendel; Kringelbach, Morten L; Deco, Gustavo; Carhart-Harris, Robin L

2017-12-15

Recent studies have started to elucidate the effects of lysergic acid diethylamide (LSD) on the human brain but the underlying dynamics are not yet fully understood. Here we used 'connectome-harmonic decomposition', a novel method to investigate the dynamical changes in brain states. We found that LSD alters the energy and the power of individual harmonic brain states in a frequency-selective manner. Remarkably, this leads to an expansion of the repertoire of active brain states, suggestive of a general re-organization of brain dynamics given the non-random increase in co-activation across frequencies. Interestingly, the frequency distribution of the active repertoire of brain states under LSD closely follows power-laws indicating a re-organization of the dynamics at the edge of criticality. Beyond the present findings, these methods open up for a better understanding of the complex brain dynamics in health and disease.

In situ carbon turnover dynamics and the role of soil microorganisms therein: a climate warming study in an Alpine ecosystem.

PubMed

Djukic, Ika; Zehetner, Franz; Watzinger, Andrea; Horacek, Micha; Gerzabek, Martin H

2013-01-01

Litter decomposition represents one of the largest fluxes in the global terrestrial carbon cycle. The aim of this study was to improve our understanding of the factors governing decomposition in alpine ecosystems and how their responses to changing environmental conditions change over time. Our study area stretches over an elevation gradient of 1000 m on the Hochschwab massif in the Northern Limestone Alps of Austria. We used high-to-low elevation soil translocation to simulate the combined effects of changing climatic conditions, shifting vegetation zones, and altered snow cover regimes. In original and translocated soils, we conducted in situ decomposition experiments with maize litter and studied carbon turnover dynamics as well as temporal response patterns of the pathways of carbon during microbial decomposition over a 2-year incubation period. A simulated mean annual soil warming (through down-slope translocation) of 1.5 and 2.7 °C, respectively, resulted in a significantly accelerated turnover of added maize carbon. Changes in substrate quantity and quality in the course of the decomposition appeared to have less influence on the microbial community composition and its substrate utilization than the prevailing environmental/site conditions, to which the microbial community adapted quickly upon change. In general, microbial community composition and function significantly affected substrate decomposition rates only in the later stage of decomposition when the differentiation in substrate use among the microbial groups became more evident. Our study demonstrated that rising temperatures in alpine ecosystems may accelerate decomposition of litter carbon and also lead to a rapid adaptation of the microbial communities to the new environmental conditions. © 2012 Federation of European Microbiological Societies. Published by Blackwell Publishing Ltd. All rights reserved.

Predicting the decomposition of Scots pine, Norway spruce, and birch stems in Finland.

PubMed

Mäkinen, Harri; Hynynen, Jari; Siitonen, Juha; Sievänen, Risto

2006-10-01

Models were developed for predicting the decomposition of dead wood for the main tree species in Finland, based on data collected from long-term thinning experiments in southern and central Finland. The decomposition rates were strongly related to the number of years after tree death. In contrast to previous studies, which have used the first-order exponential model, we found that the decomposition rate was not constant. Therefore, the Gompertz and Chapman-Richard's functions were fitted to the data. The slow initial decomposition period was mainly due to the fact that most dead trees remained standing as snags after their death. The initial period was followed by a period of rapid decomposition and, finally, by a period of moderately slow decomposition. Birch stems decomposed more rapidly than Scots pine and Norway spruce stems. Decomposition rates of Norway spruce stems were somewhat lower than those of Scots pine. Because the carbon concentration of decaying boles was relatively stable (about 50%) the rate of carbon loss follows that of mass loss. Models were also developed for the probability that a dead tree remains standing as a snag. During the first years after death, the probability was high. Thereafter, it decreased rapidly, the decrease being faster for birch stems than for Scots pine and Norway spruce stems. Almost all stems had fallen down within 40 years after their death. In Scots pine and Norway spruce, most snags remained hard and belonged to decay class 1. In birch, a higher proportion of snags belonged to the more advanced decay classes. The models provide a framework for predicting dead wood dynamics in managed as well as dense unthinned stands. The models can be incorporated into forest management planning systems, thereby facilitating estimates of carbon dynamics.

Singular value decomposition metrics show limitations of detector design in diffuse fluorescence tomography

PubMed Central

Leblond, Frederic; Tichauer, Kenneth M.; Pogue, Brian W.

2010-01-01

The spatial resolution and recovered contrast of images reconstructed from diffuse fluorescence tomography data are limited by the high scattering properties of light propagation in biological tissue. As a result, the image reconstruction process can be exceedingly vulnerable to inaccurate prior knowledge of tissue optical properties and stochastic noise. In light of these limitations, the optimal source-detector geometry for a fluorescence tomography system is non-trivial, requiring analytical methods to guide design. Analysis of the singular value decomposition of the matrix to be inverted for image reconstruction is one potential approach, providing key quantitative metrics, such as singular image mode spatial resolution and singular data mode frequency as a function of singular mode. In the present study, these metrics are used to analyze the effects of different sources of noise and model errors as related to image quality in the form of spatial resolution and contrast recovery. The image quality is demonstrated to be inherently noise-limited even when detection geometries were increased in complexity to allow maximal tissue sampling, suggesting that detection noise characteristics outweigh detection geometry for achieving optimal reconstructions. PMID:21258566

Investigation of Kelvin wave periods during Hai-Tang typhoon using Empirical Mode Decomposition

NASA Astrophysics Data System (ADS)

Kishore, P.; Jayalakshmi, J.; Lin, Pay-Liam; Velicogna, Isabella; Sutterley, Tyler C.; Ciracì, Enrico; Mohajerani, Yara; Kumar, S. Balaji

2017-11-01

Equatorial Kelvin waves (KWs) are fundamental components of the tropical climate system. In this study, we investigate Kelvin waves (KWs) during the Hai-Tang typhoon of 2005 using Empirical Mode Decomposition (EMD) of regional precipitation, zonal and meridional winds. For the analysis, we use daily precipitation datasets from the Global Precipitation Climatology Project (GPCP) and wind datasets from the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-analysis (ERA-Interim). As an additional measurement, we use in-situ precipitation datasets from rain-gauges over the Taiwan region. The maximum accumulated precipitation was approximately 2400 mm during the period July 17-21, 2005 over the southwestern region of Taiwan. The spectral analysis using the wind speed at 950 hPa found in the 2nd, 3rd, and 4th intrinsic mode functions (IMFs) reveals prevailing Kelvin wave periods of ∼3 days, ∼4-6 days, and ∼6-10 days, respectively. From our analysis of precipitation datasets, we found the Kelvin waves oscillated with periods between ∼8 and 20 days.

Investigation of KDP crystal surface based on an improved bidimensional empirical mode decomposition method

NASA Astrophysics Data System (ADS)

Lu, Lei; Yan, Jihong; Chen, Wanqun; An, Shi

2018-03-01

This paper proposed a novel spatial frequency analysis method for the investigation of potassium dihydrogen phosphate (KDP) crystal surface based on an improved bidimensional empirical mode decomposition (BEMD) method. Aiming to eliminate end effects of the BEMD method and improve the intrinsic mode functions (IMFs) for the efficient identification of texture features, a denoising process was embedded in the sifting iteration of BEMD method. With removing redundant information in decomposed sub-components of KDP crystal surface, middle spatial frequencies of the cutting and feeding processes were identified. Comparative study with the power spectral density method, two-dimensional wavelet transform (2D-WT), as well as the traditional BEMD method, demonstrated that the method developed in this paper can efficiently extract texture features and reveal gradient development of KDP crystal surface. Furthermore, the proposed method was a self-adaptive data driven technique without prior knowledge, which overcame shortcomings of the 2D-WT model such as the parameters selection. Additionally, the proposed method was a promising tool for the application of online monitoring and optimal control of precision machining process.

Transmission and reflection of terahertz plasmons in two-dimensional plasmonic devices

DOE PAGES

Sydoruk, Oleksiy; Choonee, Kaushal; Dyer, Gregory Conrad

2015-03-10

We found that plasmons in two-dimensional semiconductor devices will be reflected by discontinuities, notably, junctions between gated and non-gated electron channels. The transmitted and reflected plasmons can form spatially- and frequency-varying signals, and their understanding is important for the design of terahertz detectors, oscillators, and plasmonic crystals. Using mode decomposition, we studied terahertz plasmons incident on a junction between a gated and a nongated channel. The plasmon reflection and transmission coefficients were found numerically and analytically and studied between 0.3 and 1 THz for a range of electron densities. At higher frequencies, we could describe the plasmons by a simplifiedmore » model of channels in homogeneous dielectrics, for which the analytical approximations were accurate. At low frequencies, however, the full geometry and mode spectrum had to be taken into account. Moreover, the results agreed with simulations by the finite-element method. As a result, mode decomposition thus proved to be a powerful method for plasmonic devices, combining the rigor of complete solutions of Maxwell's equations with the convenience of analytical expressions.« less

Causality analysis of leading singular value decomposition modes identifies rotor as the dominant driving normal mode in fibrillation

NASA Astrophysics Data System (ADS)

Biton, Yaacov; Rabinovitch, Avinoam; Braunstein, Doron; Aviram, Ira; Campbell, Katherine; Mironov, Sergey; Herron, Todd; Jalife, José; Berenfeld, Omer

2018-01-01

Cardiac fibrillation is a major clinical and societal burden. Rotors may drive fibrillation in many cases, but their role and patterns are often masked by complex propagation. We used Singular Value Decomposition (SVD), which ranks patterns of activation hierarchically, together with Wiener-Granger causality analysis (WGCA), which analyses direction of information among observations, to investigate the role of rotors in cardiac fibrillation. We hypothesized that combining SVD analysis with WGCA should reveal whether rotor activity is the dominant driving force of fibrillation even in cases of high complexity. Optical mapping experiments were conducted in neonatal rat cardiomyocyte monolayers (diameter, 35 mm), which were genetically modified to overexpress the delayed rectifier K+ channel IKr only in one half of the monolayer. Such monolayers have been shown previously to sustain fast rotors confined to the IKr overexpressing half and driving fibrillatory-like activity in the other half. SVD analysis of the optical mapping movies revealed a hierarchical pattern in which the primary modes corresponded to rotor activity in the IKr overexpressing region and the secondary modes corresponded to fibrillatory activity elsewhere. We then applied WGCA to evaluate the directionality of influence between modes in the entire monolayer using clear and noisy movies of activity. We demonstrated that the rotor modes influence the secondary fibrillatory modes, but influence was detected also in the opposite direction. To more specifically delineate the role of the rotor in fibrillation, we decomposed separately the respective SVD modes of the rotor and fibrillatory domains. In this case, WGCA yielded more information from the rotor to the fibrillatory domains than in the opposite direction. In conclusion, SVD analysis reveals that rotors can be the dominant modes of an experimental model of fibrillation. Wiener-Granger causality on modes of the rotor domains confirms their preferential driving influence on fibrillatory modes.

Intelligent diagnosis of short hydraulic signal based on improved EEMD and SVM with few low-dimensional training samples

NASA Astrophysics Data System (ADS)

Zhang, Meijun; Tang, Jian; Zhang, Xiaoming; Zhang, Jiaojiao

2016-03-01

The high accurate classification ability of an intelligent diagnosis method often needs a large amount of training samples with high-dimensional eigenvectors, however the characteristics of the signal need to be extracted accurately. Although the existing EMD(empirical mode decomposition) and EEMD(ensemble empirical mode decomposition) are suitable for processing non-stationary and non-linear signals, but when a short signal, such as a hydraulic impact signal, is concerned, their decomposition accuracy become very poor. An improve EEMD is proposed specifically for short hydraulic impact signals. The improvements of this new EEMD are mainly reflected in four aspects, including self-adaptive de-noising based on EEMD, signal extension based on SVM(support vector machine), extreme center fitting based on cubic spline interpolation, and pseudo component exclusion based on cross-correlation analysis. After the energy eigenvector is extracted from the result of the improved EEMD, the fault pattern recognition based on SVM with small amount of low-dimensional training samples is studied. At last, the diagnosis ability of improved EEMD+SVM method is compared with the EEMD+SVM and EMD+SVM methods, and its diagnosis accuracy is distinctly higher than the other two methods no matter the dimension of the eigenvectors are low or high. The improved EEMD is very propitious for the decomposition of short signal, such as hydraulic impact signal, and its combination with SVM has high ability for the diagnosis of hydraulic impact faults.

The influence of litter composition across the litter–soil interface on mass loss, nitrogen dynamics and the decomposer community

EPA Science Inventory

Many studies have investigated the influence of plant litter species composition on decomposition, but results have been context-dependent. Litter and soil are considered to constitute a decomposition continuum, but whether litter and soil ecosystems respond to litter identity an...

Soil coverage reduce photodegradation and promotes the development of soil-microbial films on dryland leaf litter

USDA-ARS?s Scientific Manuscript database

Litter decomposition is a central focus of ecosystem science because of its importance to biogeochemical pools and cycling, but predicting dryland decomposition dynamics is problematic. Some studies indicate photodegradation by ultraviolet (UV) radiation can be a significant driver of dryland decomp...

Leaf Litter Decomposition and Nutrient Dynamics in Four Southern Forested Floodplain Communities

Treesearch

Terrell T. Baker; B. Graeme Lockaby; William H. Conner; Calvin E. Meier; John A. Stanturf

2001-01-01

Decomposition of site-specific litter mixtures was monitored for 100 wk in four Roodplaht communities: (i) a mixed oak community along the Cache River in central Arkansas, (ii) a sweetgum (Liquidambar styraciflua L.)-cherrybark oak (Quercus falcata var. pagodaefolia Ell.) community along Iatt Creek in...

Decomposition dynamic of two aquatic macrophytes Trapa bispinosa Roxb. and Nelumbo nucifera detritus.

PubMed

Zhou, Xiaohong; Feng, Deyou; Wen, Chunzi; Liu, Dan

2018-03-29

In freshwater ecosystems, aquatic macrophytes play significant roles in nutrient cycling. One problem in this process is nutrient loss in the tissues of untimely harvested plants. In this study, we used two aquatic species, Nelumbo nucifera and Trapa bispinosa Roxb., to investigate the decomposition dynamics and nutrient release from detritus. Litter bags containing 10 g of stems (plus petioles) and leaves for each species detritus were incubated in the pond from November 2016 to May 2017. Nine times litterbags were retrieved on days 6, 14, 25, 45, 65, 90, 125, 145, and 165 after the decomposition experiment for the monitoring of biomass loss and nutrient release. The results suggested that the dry masses of N. nucifera and T. bispinosa decomposed by 49.35-69.40 and 82.65-91.65%, respectively. The order of decomposition rate constants (k) is as follows: leaves of T. bispinosa (0.0122 day -1 ) > stems (plus petioles) of T. bispinosa (0.0090 day -1 ) > leaves of N. nucifera (0.0060 day -1 ) > stems (plus petioles) of N. nucifera (0.0030 day -1 ). Additionally, the orders of time for 50% dry mass decay, time for 95% dry mass decay, and turnover rate are as follows: leaves < stems (plus petioles) and T. bispinosa < N. nucifera, respectively. This result indicated that the dry mass loss, k values, and other parameters related to k values are significantly different in species- and tissue-specific. The C, N, and P concentration and the C/N, C/P, and N/P ratios presented the irregular temporal changes trends during the whole decay period. In addition, nutrient accumulation index (AI) was significantly changed depending on the dry mass remaining and C, N, and P concentration in detritus at different decomposition times. The nutrient AIs were 36.72, 8.08, 6.35, and 2.56% for N; 31.25, 9.85, 4.00, and 1.63% for P; 25.15, 16.96, 7.36, and 6.16% for C in the stems (plus petioles) of N. nucifera, leaves of N. nucifera, stems (plus petioles) of T. bispinosa, and leaves of T. bispinosa, respectively, at the day 165. These results indicated that 63.28-97.44% of N, 68.75-98.37% of P, and 74.85-93.84% of C were released from the plant detritus to the water at the day 165 of the decomposition period. The initial detritus chemistry, particularly the P-related parameters (P concentration and C/P and N/P ratios), strongly affected dry mass loss, decomposition rates, and nutrient released from detritus into water. Two-way ANOVA results also confirm that the effects on the species were significant for decomposition dynamics (dry mass loss), nutrient release (nutrient concentration, their ratios, and nutrient AI) (P < 0.01), and expected N concentration (P > 0.05). In addition, the decomposition time had also significant effects on the detritus decomposition dynamic and nutrient release. However, the contributors of species and decomposition time on detritus decomposition were significantly different on the basis of their F values of two-way ANOVA results. This study can provide scientific bases for the aquatic plant scientific management in freshwater ecosystems of the East region of China.

Extremal black holes in dynamical Chern-Simons gravity

NASA Astrophysics Data System (ADS)

McNees, Robert; Stein, Leo C.; Yunes, Nicolás

2016-12-01

Rapidly rotating black hole (BH) solutions in theories beyond general relativity (GR) play a key role in experimental gravity, as they allow us to compute observables in extreme spacetimes that deviate from the predictions of GR. Such solutions are often difficult to find in beyond-general-relativity theories due to the inclusion of additional fields that couple to the metric nonlinearly and non-minimally. In this paper, we consider rotating BH solutions in one such theory, dynamical Chern-Simons (dCS) gravity, where the Einstein-Hilbert action is modified by the introduction of a dynamical scalar field that couples to the metric through the Pontryagin density. We treat dCS gravity as an effective field theory and work in the decoupling limit, where corrections are treated as small perturbations from GR. We perturb about the maximally rotating Kerr solution, the so-called extremal limit, and develop mathematical insight into the analysis techniques needed to construct solutions for generic spin. First we find closed-form, analytic expressions for the extremal scalar field, and then determine the trace of the metric perturbation, giving both in terms of Legendre decompositions. Retaining only the first three and four modes in the Legendre representation of the scalar field and the trace, respectively, suffices to ensure a fidelity of over 99% relative to full numerical solutions. The leading-order mode in the Legendre expansion of the trace of the metric perturbation contains a logarithmic divergence at the extremal Kerr horizon, which is likely to be unimportant as it occurs inside the perturbed dCS horizon. The techniques employed here should enable the construction of analytic, closed-form expressions for the scalar field and metric perturbations on a background with arbitrary rotation.

Is it worth hyperaccumulating Ni on non-serpentine soils? Decomposition dynamics of mixed-species litters containing hyperaccumulated Ni across serpentine and non-serpentine environments.

PubMed

Adamidis, George C; Kazakou, Elena; Aloupi, Maria; Dimitrakopoulos, Panayiotis G

2016-06-01

Nickel (Ni)-hyperaccumulating species produce high-Ni litters and may potentially influence important ecosystem processes such as decomposition. Although litters resembling the natural community conditions are essential in order to predict decomposition dynamics, decomposition of mixed-species litters containing hyperaccumulated Ni has never been studied. This study aims to test the effect of different litter mixtures containing hyperaccumulated Ni on decomposition and Ni release across serpentine and non-serpentine soils. Three different litter mixtures were prepared based on the relative abundance of the dominant species in three serpentine soils in the island of Lesbos, Greece where the Ni-hyperaccumulator Alyssum lesbiacum is present. Each litter mixture decomposed on its original serpentine habitat and on an adjacent non-serpentine habitat, in order to investigate whether the decomposition rates differ across the contrasted soils. In order to make comparisons across litter mixtures and to investigate whether additive or non-additive patterns of mass loss occur, a control non-serpentine site was used. Mass loss and Ni release were measured after 90, 180 and 270 d of field exposure. The decomposition rates and Ni release had higher values on serpentine soils after all periods of field exposure. The recorded rapid release of hyperaccumulated Ni is positively related to the initial litter Ni concentration. No differences were found in the decomposition of the three different litter mixtures at the control non-serpentine site, while their patterns of mass loss were additive. Our results: (1) demonstrate the rapid decomposition of litters containing hyperaccumulated Ni on serpentine soils, indicating the presence of metal-tolerant decomposers; and (2) imply the selective decomposition of low-Ni parts of litters by the decomposers on non-serpentine soils. This study provides support for the elemental allelopathy hypothesis of hyperaccumulation, presenting the potential selective advantages acquired by metal-hyperaccumulating plants through litter decomposition on serpentine soils. © The Author 2016. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

A model-reduction approach in micromechanics of materials preserving the variational structure of constitutive relations

NASA Astrophysics Data System (ADS)

Michel, Jean-Claude; Suquet, Pierre

2016-05-01

In 2003 the authors proposed a model-reduction technique, called the Nonuniform Transformation Field Analysis (NTFA), based on a decomposition of the local fields of internal variables on a reduced basis of modes, to analyze the effective response of composite materials. The present study extends and improves on this approach in different directions. It is first shown that when the constitutive relations of the constituents derive from two potentials, this structure is passed to the NTFA model. Another structure-preserving model, the hybrid NTFA model of Fritzen and Leuschner, is analyzed and found to differ (slightly) from the primal NTFA model (it does not exhibit the same variational upper bound character). To avoid the "on-line" computation of local fields required by the hybrid model, new reduced evolution equations for the reduced variables are proposed, based on an expansion to second order (TSO) of the potential of the hybrid model. The coarse dynamics can then be entirely expressed in terms of quantities which can be pre-computed once for all. Roughly speaking, these pre-computed quantities depend only on the average and fluctuations per phase of the modes and of the associated stress fields. The accuracy of the new NTFA-TSO model is assessed by comparison with full-field simulations. The acceleration provided by the new coarse dynamics over the full-field computations (and over the hybrid model) is then spectacular, larger by three orders of magnitude than the acceleration due to the sole reduction of unknowns.

Molecular basis of HHQ biosynthesis: molecular dynamics simulations, enzyme kinetic and surface plasmon resonance studies

PubMed Central

2013-01-01

Background PQS (PseudomonasQuinolone Signal) and its precursor HHQ are signal molecules of the P. aeruginosa quorum sensing system. They explicate their role in mammalian pathogenicity by binding to the receptor PqsR that induces virulence factor production and biofilm formation. The enzyme PqsD catalyses the biosynthesis of HHQ. Results Enzyme kinetic analysis and surface plasmon resonance (SPR) biosensor experiments were used to determine mechanism and substrate order of the biosynthesis. Comparative analysis led to the identification of domains involved in functionality of PqsD. A kinetic cycle was set up and molecular dynamics (MD) simulations were used to study the molecular bases of the kinetics of PqsD. Trajectory analysis, pocket volume measurements, binding energy estimations and decompositions ensured insights into the binding mode of the substrates anthraniloyl-CoA and β-ketodecanoic acid. Conclusions Enzyme kinetics and SPR experiments hint at a ping-pong mechanism for PqsD with ACoA as first substrate. Trajectory analysis of different PqsD complexes evidenced ligand-dependent induced-fit motions affecting the modified ACoA funnel access to the exposure of a secondary channel. A tunnel-network is formed in which Ser317 plays an important role by binding to both substrates. Mutagenesis experiments resulting in the inactive S317F mutant confirmed the importance of this residue. Two binding modes for β-ketodecanoic acid were identified with distinct catalytic mechanism preferences. PMID:23916145

Characterization of a Heated Liquid Jet in Crossflow

NASA Astrophysics Data System (ADS)

Wiest, Heather K.

The liquid jet in crossflow (LJICF) is a widely utilized fuel injection method for airbreathing propulsion devices such as low NO x gas turbine combustors, turbojet afterburners, scramjet/ramjet engines, and rotating detonation engines (RDE's). This flow field allows for efficient fuel-air mixing as aerodynamic forces from the crossflow augment atomization. Additionally, increases in the thermal demands of advanced aeroengines necessitates the use of fuel as a primary coolant. The resulting higher fuel temperatures can cause flash atomization of the liquid fuel as it is injected into a crossflow, potentially leading to a large reduction in the jet penetration. While many experimental works have characterized the overall atomization process of a room temperature liquid jet in an ambient temperature and pressure crossflow, the aggressive conditions associated with flash atomization especially in an air crossflow with elevated temperatures and pressures have been less studied in the community. A successful test campaign was conducted to study the effects of fuel temperature on a liquid jet injected transversely into a steady air crossflow at ambient as well as elevated temperature and pressure conditions. Modifications were made to an existing optically accessible rig, and a new fuel injector was designed for this study. Backlit imaging was utilized to record changes in the overall spray characteristics and jet trajectory as fuel temperature and crossflow conditioners were adjusted. Three primary analysis techniques were applied to the heated LJICF data: linear regression of detected edges to determine trajectory correlations, exploratory study of pixel intensity variations both temporally as well as spatially, and modal decomposition of the data. The overall objectives of this study was to assess the trajectory, breakup, and mixing of the LJICF undery varying jet and crossflow conditions, develop a trajectory correlation to predict changes in jet penetration due to fuel temperature increases, and characterize the changes in underlying physics in the LJICF flow field. Based on visual inspection, the increase in fuel temperature leads to a finer and denser fuel spray. With increasingly elevated liquid temperatures, the penetration of the jet typically decreases. At or near flashing conditions, the jet had a tendency to penetrate upstream before bending over in the crossflow as well as experiences a rapid expansion causing the jet column to increase in width. Two trajectory correlations were determined, one for each set of crossflow conditions, based on normalized axial distance, normalized liquid viscosity, and normalized jet diameter as liquid is vaporized. The pixel intensity analysis showed that the highest temperature jet in the ambient temperature and pressure crossflow exhibited periodic behavior that was also found using various modal techniques including proper orthogonal decomposition and dynamic mode decomposition. Dominant frequencies determined for most test cases were associated with the bulk or flapping motion of the jet. Most notably, the DMD analysis in this study was successful in identifying robust modes across different subgroupings of the data even though the modes identified were not the highest power modes in each DMD spectrum.

Intracavity vortex beam generation

NASA Astrophysics Data System (ADS)

Naidoo, Darryl; Aït-Ameur, Kamel; Forbes, Andrew

2011-10-01

In this paper we explore vortex beams and in particular the generation of single LG0l modes and superpositions thereof. Vortex beams carry orbital angular momentum (OAM) and this intrinsic property makes them prevalent in transferring this OAM to matter and to be used in quantum information processing. We explore an extra-cavity and intra-cavity approach in LG0l mode generation respectively. The outputs of a Porro-prism resonator are represented by "petals" and we show that through a full modal decomposition, the "petal" fields are a superposition of two LG0l modes.

Vibrational Softening of a Protein on Ligand Binding

DOE Office of Scientific and Technical Information (OSTI.GOV)

Balog, Erica; Perahia, David; Smith, Jeremy C

2011-01-01

Neutron scattering experiments have demonstrated that binding of the cancer drug methotrexate softens the low-frequency vibrations of its target protein, dihydrofolate reductase (DHFR). Here, this softening is fully reproduced using atomic detail normal-mode analysis. Decomposition of the vibrational density of states demonstrates that the largest contributions arise from structural elements of DHFR critical to stability and function. Mode-projection analysis reveals an increase of the breathing-like character of the affected vibrational modes consistent with the experimentally observed increased adiabatic compressibility of the protein on complexation.

Incorporating microbial dormancy dynamics into soil decomposition models to improve quantification of soil carbon dynamics of northern temperate forests

NASA Astrophysics Data System (ADS)

He, Yujie; Yang, Jinyan; Zhuang, Qianlai; Harden, Jennifer W.; McGuire, Anthony D.; Liu, Yaling; Wang, Gangsheng; Gu, Lianhong

2015-12-01

Soil carbon dynamics of terrestrial ecosystems play a significant role in the global carbon cycle. Microbial-based decomposition models have seen much growth recently for quantifying this role, yet dormancy as a common strategy used by microorganisms has not usually been represented and tested in these models against field observations. Here we developed an explicit microbial-enzyme decomposition model and examined model performance with and without representation of microbial dormancy at six temperate forest sites of different forest types. We then extrapolated the model to global temperate forest ecosystems to investigate biogeochemical controls on soil heterotrophic respiration and microbial dormancy dynamics at different temporal-spatial scales. The dormancy model consistently produced better match with field-observed heterotrophic soil CO2 efflux (RH) than the no dormancy model. Our regional modeling results further indicated that models with dormancy were able to produce more realistic magnitude of microbial biomass (<2% of soil organic carbon) and soil RH (7.5 ± 2.4 Pg C yr-1). Spatial correlation analysis showed that soil organic carbon content was the dominating factor (correlation coefficient = 0.4-0.6) in the simulated spatial pattern of soil RH with both models. In contrast to strong temporal and local controls of soil temperature and moisture on microbial dormancy, our modeling results showed that soil carbon-to-nitrogen ratio (C:N) was a major regulating factor at regional scales (correlation coefficient = -0.43 to -0.58), indicating scale-dependent biogeochemical controls on microbial dynamics. Our findings suggest that incorporating microbial dormancy could improve the realism of microbial-based decomposition models and enhance the integration of soil experiments and mechanistically based modeling.

Incorporating microbial dormancy dynamics into soil decomposition models to improve quantification of soil carbon dynamics of northern temperate forests

DOE Office of Scientific and Technical Information (OSTI.GOV)

He, Yujie; Yang, Jinyan; Zhuang, Qianlai

Soil carbon dynamics of terrestrial ecosystems play a significant role in the global carbon cycle. Microbial-based decomposition models have seen much growth recently for quantifying this role, yet dormancy as a common strategy used by microorganisms has not usually been represented and tested in these models against field observations. Here in this study we developed an explicit microbial-enzyme decomposition model and examined model performance with and without representation of microbial dormancy at six temperate forest sites of different forest types. We then extrapolated the model to global temperate forest ecosystems to investigate biogeochemical controls on soil heterotrophic respiration and microbialmore » dormancy dynamics at different temporal-spatial scales. The dormancy model consistently produced better match with field-observed heterotrophic soil CO 2 efflux (R H) than the no dormancy model. Our regional modeling results further indicated that models with dormancy were able to produce more realistic magnitude of microbial biomass (<2% of soil organic carbon) and soil R H (7.5 ± 2.4 PgCyr -1). Spatial correlation analysis showed that soil organic carbon content was the dominating factor (correlation coefficient = 0.4-0.6) in the simulated spatial pattern of soil R H with both models. In contrast to strong temporal and local controls of soil temperature and moisture on microbial dormancy, our modeling results showed that soil carbon-to-nitrogen ratio (C:N) was a major regulating factor at regional scales (correlation coefficient = -0.43 to -0.58), indicating scale-dependent biogeochemical controls on microbial dynamics. Our findings suggest that incorporating microbial dormancy could improve the realism of microbial-based decomposition models and enhance the integration of soil experiments and mechanistically based modeling.« less

Incorporating microbial dormancy dynamics into soil decomposition models to improve quantification of soil carbon dynamics of northern temperate forests

USGS Publications Warehouse

He, Yujie; Yang, Jinyan; Zhuang, Qianlai; Harden, Jennifer W.; McGuire, A. David; Liu, Yaling; Wang, Gangsheng; Gu, Lianhong

2015-01-01

Soil carbon dynamics of terrestrial ecosystems play a significant role in the global carbon cycle. Microbial-based decomposition models have seen much growth recently for quantifying this role, yet dormancy as a common strategy used by microorganisms has not usually been represented and tested in these models against field observations. Here we developed an explicit microbial-enzyme decomposition model and examined model performance with and without representation of microbial dormancy at six temperate forest sites of different forest types. We then extrapolated the model to global temperate forest ecosystems to investigate biogeochemical controls on soil heterotrophic respiration and microbial dormancy dynamics at different temporal-spatial scales. The dormancy model consistently produced better match with field-observed heterotrophic soil CO2 efflux (RH) than the no dormancy model. Our regional modeling results further indicated that models with dormancy were able to produce more realistic magnitude of microbial biomass (<2% of soil organic carbon) and soil RH (7.5 ± 2.4 Pg C yr−1). Spatial correlation analysis showed that soil organic carbon content was the dominating factor (correlation coefficient = 0.4–0.6) in the simulated spatial pattern of soil RHwith both models. In contrast to strong temporal and local controls of soil temperature and moisture on microbial dormancy, our modeling results showed that soil carbon-to-nitrogen ratio (C:N) was a major regulating factor at regional scales (correlation coefficient = −0.43 to −0.58), indicating scale-dependent biogeochemical controls on microbial dynamics. Our findings suggest that incorporating microbial dormancy could improve the realism of microbial-based decomposition models and enhance the integration of soil experiments and mechanistically based modeling.

Noncatalytic hydrazine thruster development - 0.050 to 5.0 pounds thrust

NASA Technical Reports Server (NTRS)

Murch, C. K.; Sackheim, R. L.; Kuenzly, J. D.; Callens, R. A.

1976-01-01

Noncatalytic (thermal-decompositon) hydrazine thrusters can operate in both the pulsing and steady-state modes to meet the propulsive requirements of long-life spacecraft. The thermal decomposition mode yields higher specific impulse than is characteristic of catalytic thrusters at similar thrust levels. This performance gain is the result of higher temperature operation and a lower fraction of ammonia dissociation. Some life limiting factors of catalytic thrusters are eliminated.

High heating rate decomposition dynamics of copper oxide by nanocalorimetry-coupled time-of-flight mass spectrometry

NASA Astrophysics Data System (ADS)

Yi, Feng; DeLisio, Jeffery B.; Nguyen, Nam; Zachariah, Michael R.; LaVan, David A.

2017-12-01

The thermodynamics and evolved gases were measured during the rapid decomposition of copper oxide (CuO) thin film at rates exceeding 100,000 K/s. CuO decomposes to release oxygen when heated and serves as an oxidizer in reactive composites and chemical looping combustion. Other instruments have shown either one or two decomposition steps during heating. We have confirmed that CuO decomposes by two steps at both slower and higher heating rates. The decomposition path influences the reaction course in reactive Al/CuO/Al composites, and full understanding is important in designing reactive mixtures and other new reactive materials.

Self-force via m-mode regularization and 2+1D evolution. II. Scalar-field implementation on Kerr spacetime

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dolan, Sam R.; Barack, Leor; Wardell, Barry

2011-10-15

This is the second in a series of papers aimed at developing a practical time-domain method for self-force calculations in Kerr spacetime. The key elements of the method are (i) removal of a singular part of the perturbation field with a suitable analytic 'puncture' based on the Detweiler-Whiting decomposition, (ii) decomposition of the perturbation equations in azimuthal (m-)modes, taking advantage of the axial symmetry of the Kerr background, (iii) numerical evolution of the individual m-modes in 2+1 dimensions with a finite-difference scheme, and (iv) reconstruction of the physical self-force from the mode sum. Here we report an implementation of themore » method to compute the scalar-field self-force along circular equatorial geodesic orbits around a Kerr black hole. This constitutes a first time-domain computation of the self-force in Kerr geometry. Our time-domain code reproduces the results of a recent frequency-domain calculation by Warburton and Barack, but has the added advantage of being readily adaptable to include the backreaction from the self-force in a self-consistent manner. In a forthcoming paper--the third in the series--we apply our method to the gravitational self-force (in the Lorenz gauge).« less

Dynamic Regimes of El Niño Southern Oscillation and Influenza Pandemic Timing

PubMed Central

Oluwole, Olusegun Steven Ayodele

2017-01-01

El Niño southern oscillation (ENSO) dynamics has been shown to drive seasonal influenza dynamics. Severe seasonal influenza epidemics and the 2009–2010 pandemic were coincident with chaotic regime of ENSO dynamics. ENSO dynamics from 1876 to 2016 were characterized to determine if influenza pandemics are coupled to chaotic regimes. Time-varying spectra of southern oscillation index (SOI) and sea surface temperature (SST) were compared. SOI and SST were decomposed to components using the algorithm of noise-assisted multivariate empirical mode decomposition. The components were Hilbert transformed to generate instantaneous amplitudes and phases. The trajectories and attractors of components were characterized in polar coordinates and state space. Influenza pandemics were mapped to dynamic regimes of SOI and SST joint recurrence of annual components. State space geometry of El Niños lagged by influenza pandemics were characterized and compared with other El Niños. Timescales of SOI and SST components ranged from sub-annual to multidecadal. The trajectories of SOI and SST components and the joint recurrence of annual components were dissipative toward chaotic attractors. Periodic, quasi-periodic, and chaotic regimes were present in the recurrence of trajectories, but chaos–chaos transitions dominated. Influenza pandemics occurred during chaotic regimes of significantly low transitivity dimension (p < 0.0001). El Niños lagged by influenza pandemics had distinct state space geometry (p < 0.0001). Chaotic dynamics explains the aperiodic timing, and varying duration and strength of El Niños. Coupling of all influenza pandemics of the past 140 years to chaotic regimes of low transitivity indicate that ENSO dynamics drives influenza pandemic dynamics. Forecasts models from ENSO dynamics should compliment surveillance for novel influenza viruses. PMID:29218303

Dynamic response of peatbank moss communities to hydroclimate over the last 2000 years in the western Antarctic Peninsula

NASA Astrophysics Data System (ADS)

Stelling, J.; Yu, Z.; Beilman, D. W.

2016-12-01

The western Antarctic Peninsula experienced rapid warming in late half of the 20th century in part due to a positive phase of the Southern Annular Mode (SAM) causing poleward expansion of the southern westerly wind belt that brings warmer and moister air to the peninsula. However, we do not know how coastal terrestrial ecosystems have responded to changes in temperature and hydroclimate. Here we present a paleoecological and geochemical record of ecosystem history derived from late Holocene peatbank deposits on Litchfield Island (64°46'S; 64°06'W) to reconstruct terrestrial response to temperature and hydroclimate fluctuations. Chronology of our 80-cm-long peat core from the north-facing slope is constrained by 11 AMS 14C dates covering the last 2500 years. Our macrofossil results show that relative abundance of the two dominant moss species fluctuates between <10 and 90%. Furthermore, the δ13C values of bulk peat range from -26.4 to -22.1‰ that mostly reflects species relative abundance change through time. The periods with C:N values of <20—lower than the expected C:N values (40 to 80) of fresh moss plants—corresponds with intervals containing abundant fine debris (>50%), indicating greater decomposition and selective removal of carbon from peat. Our record shows that periods where moss dominance shifts to Polytrichum, a dry and cold tolerant moss, peat decomposition increases, and coincides with periods of negative SAM. Conversely, dominance shifts to Chorisodontium, a less drought tolerant moss, with decomposition decreased during periods of strong positive SAM. This study demonstrates that ecosystem structure and geochemical signature within these moss peatbanks is sensitive to regional moisture change that can potentially be used to reconstruct shifts in hydroclimate and possibly atmospheric circulation.

The predictive power of singular value decomposition entropy for stock market dynamics

NASA Astrophysics Data System (ADS)

Caraiani, Petre

2014-01-01

We use a correlation-based approach to analyze financial data from the US stock market, both daily and monthly observations from the Dow Jones. We compute the entropy based on the singular value decomposition of the correlation matrix for the components of the Dow Jones Industrial Index. Based on a moving window, we derive time varying measures of entropy for both daily and monthly data. We find that the entropy has a predictive ability with respect to stock market dynamics as indicated by the Granger causality tests.

Ab initio molecular dynamics simulations of the initial stages of solid-electrolyte interphase formation on lithium ion battery graphitic anodes.

PubMed

Leung, Kevin; Budzien, Joanne L

2010-07-07

The decomposition of ethylene carbonate (EC) during the initial growth of solid-electrolyte interphase (SEI) films at the solvent-graphitic anode interface is critical to lithium ion battery operations. Ab initio molecular dynamics simulations of explicit liquid EC/graphite interfaces are conducted to study these electrochemical reactions. We show that carbon edge terminations are crucial at this stage, and that achievable experimental conditions can lead to surprisingly fast EC breakdown mechanisms, yielding decomposition products seen in experiments but not previously predicted.

A domain decomposition approach to implementing fault slip in finite-element models of quasi-static and dynamic crustal deformation

USGS Publications Warehouse

Aagaard, Brad T.; Knepley, M.G.; Williams, C.A.

2013-01-01

We employ a domain decomposition approach with Lagrange multipliers to implement fault slip in a finite-element code, PyLith, for use in both quasi-static and dynamic crustal deformation applications. This integrated approach to solving both quasi-static and dynamic simulations leverages common finite-element data structures and implementations of various boundary conditions, discretization schemes, and bulk and fault rheologies. We have developed a custom preconditioner for the Lagrange multiplier portion of the system of equations that provides excellent scalability with problem size compared to conventional additive Schwarz methods. We demonstrate application of this approach using benchmarks for both quasi-static viscoelastic deformation and dynamic spontaneous rupture propagation that verify the numerical implementation in PyLith.

Determination of the mode composition of long-wave disturbances in a supersonic flow in a hotshot wind tunnel

NASA Astrophysics Data System (ADS)

Tsyryulnikov, I. S.; Kirilovskiy, S. V.; Poplavskaya, T. V.

2016-10-01

In this paper, we describe a new method of mode decomposition of disturbances on the basis of specific features of interaction of long-wave free-stream disturbances with the shock wave and knowing the trends of changing of the conversion factors of various disturbance modes due to variations of the shock wave incidence angle. The range of admissible root-mean-square amplitudes of oscillations of vortex, entropy, and acoustic modes in the free stream generated in IT-302M was obtained by using the pressure fluctuations measured on the model surface and the calculated conversion factors.

Streak instability as an initiating mechanism of the large-scale motions in a turbulent channel flow

NASA Astrophysics Data System (ADS)

de Giovanetti, Matteo; Sung, Hyung Jin; Hwang, Yongyun

2016-11-01

The large-scale motions (or bulges) have often been believed to be formed via merge and/or growth of the near-wall hairpin vortical structures. Here, we report our observation that they can be directly generated by an instability of the amplified streaky motions in the outer region (i.e. very-large-scale motions) through the self-sustaining process. We design a LES-based numerical experiment in turbulent channel flow for Reτ = 2000 where a body forcing is implemented to artificially drive an infinitely long streaky motion in the outer layer. As the forcing amplitude is increased, it is found that a new energetic structure emerges at λx 3 4 h of the streamwise length (h is the half height of channel) particularly in the wall-normal and spanwise velocities. A careful statistical examination reveals that this structure is likely to be linked with the sinuous-mode streak instability of the amplified streak, consistent with previous theoretical studies. Application of dynamic mode decomposition to this instability further shows that the phase speed of this structure scales with the outer velocity and it is initiated around the critical layer of the streaky flow.

Information-Theoretical Quantifier of Brain Rhythm Based on Data-Driven Multiscale Representation

PubMed Central

2015-01-01

This paper presents a data-driven multiscale entropy measure to reveal the scale dependent information quantity of electroencephalogram (EEG) recordings. This work is motivated by the previous observations on the nonlinear and nonstationary nature of EEG over multiple time scales. Here, a new framework of entropy measures considering changing dynamics over multiple oscillatory scales is presented. First, to deal with nonstationarity over multiple scales, EEG recording is decomposed by applying the empirical mode decomposition (EMD) which is known to be effective for extracting the constituent narrowband components without a predetermined basis. Following calculation of Renyi entropy of the probability distributions of the intrinsic mode functions extracted by EMD leads to a data-driven multiscale Renyi entropy. To validate the performance of the proposed entropy measure, actual EEG recordings from rats (n = 9) experiencing 7 min cardiac arrest followed by resuscitation were analyzed. Simulation and experimental results demonstrate that the use of the multiscale Renyi entropy leads to better discriminative capability of the injury levels and improved correlations with the neurological deficit evaluation after 72 hours after cardiac arrest, thus suggesting an effective diagnostic and prognostic tool. PMID:26380297

Numerical modeling of the exterior-to-interior transmission of impulsive sound through three-dimensional, thin-walled elastic structures

NASA Astrophysics Data System (ADS)

Remillieux, Marcel C.; Pasareanu, Stephanie M.; Svensson, U. Peter

2013-12-01

Exterior propagation of impulsive sound and its transmission through three-dimensional, thin-walled elastic structures, into enclosed cavities, are investigated numerically in the framework of linear dynamics. A model was developed in the time domain by combining two numerical tools: (i) exterior sound propagation and induced structural loading are computed using the image-source method for the reflected field (specular reflections) combined with an extension of the Biot-Tolstoy-Medwin method for the diffracted field, (ii) the fully coupled vibro-acoustic response of the interior fluid-structure system is computed using a truncated modal-decomposition approach. In the model for exterior sound propagation, it is assumed that all surfaces are acoustically rigid. Since coupling between the structure and the exterior fluid is not enforced, the model is applicable to the case of a light exterior fluid and arbitrary interior fluid(s). The structural modes are computed with the finite-element method using shell elements. Acoustic modes are computed analytically assuming acoustically rigid boundaries and rectangular geometries of the enclosed cavities. This model is verified against finite-element solutions for the cases of rectangular structures containing one and two cavities, respectively.

Long-period quasi-periodic oscillations of a small-scale magnetic structure on the Sun

NASA Astrophysics Data System (ADS)

Kolotkov, D. Y.; Smirnova, V. V.; Strekalova, P. V.; Riehokainen, A.; Nakariakov, V. M.

2017-02-01

Aims: Long-period quasi-periodic variations of the average magnetic field in a small-scale magnetic structure on the Sun are analysed. The structure is situated at the photospheric level and is involved in a facula formation in the chromosphere. Methods: The observational signal obtained from the SDO/HMI line-of-sight magnetograms of the target structure has a non-stationary behaviour, and is therefore processed with the Hilbert-Huang Transform spectral technique. Results: The empirical decomposition of the original signal and subsequent testing of the statistical significance of its intrinsic modes reveal the presence of the white and pink noisy components for the periods shorter and longer than 10 min, respectively, and a significant oscillatory mode. The oscillation is found to have a non-stationary period growing from approximately 80 to 230 min and an increasing relative amplitude, while the mean magnetic field in the oscillating structure is seen to decrease. The observed behaviour could be interpreted either by the dynamical interaction of the structure with the boundaries of supergranula cells in the region of interest or in terms of the vortex shedding appearing during the magnetic flux emergence.

Numerical studies and metric development for validation of magnetohydrodynamic models on the HIT-SI experiment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hansen, C., E-mail: hansec@uw.edu; Columbia University, New York, New York 10027; Victor, B.

We present application of three scalar metrics derived from the Biorthogonal Decomposition (BD) technique to evaluate the level of agreement between macroscopic plasma dynamics in different data sets. BD decomposes large data sets, as produced by distributed diagnostic arrays, into principal mode structures without assumptions on spatial or temporal structure. These metrics have been applied to validation of the Hall-MHD model using experimental data from the Helicity Injected Torus with Steady Inductive helicity injection experiment. Each metric provides a measure of correlation between mode structures extracted from experimental data and simulations for an array of 192 surface-mounted magnetic probes. Numericalmore » validation studies have been performed using the NIMROD code, where the injectors are modeled as boundary conditions on the flux conserver, and the PSI-TET code, where the entire plasma volume is treated. Initial results from a comprehensive validation study of high performance operation with different injector frequencies are presented, illustrating application of the BD method. Using a simplified (constant, uniform density and temperature) Hall-MHD model, simulation results agree with experimental observation for two of the three defined metrics when the injectors are driven with a frequency of 14.5 kHz.« less

Numerical simulation of interaction of long-wave disturbances with a shock wave on a wedge for the problem of mode decomposition of supersonic flow oscillations

NASA Astrophysics Data System (ADS)

Kirilovskiy, S. V.; Poplavskaya, T. V.; Tsyryulnikov, I. S.

2016-10-01

This work is aimed at obtaining conversion factors of free stream disturbances from shock wave angle φ, angle of acoustic disturbances distribution θ and Mach number M∞ by solving a problem of interaction of long-wave (with the wavelength λ greater than the model length) free-stream disturbances with a shock wave formed in a supersonic flow around the wedge. Conversion factors at x/λ=0.2 as a ration between amplitude of pressure pulsations on the wedge surface and free stream disturbances amplitude were obtained. Factors of conversion were described by the dependence on angle θ of disturbances distribution, shock wave angle φ and Mach number M∞. These dependences are necessary for solving the problem of mode decomposition of disturbances in supersonic flows in wind tunnels.

Bi-dimensional empirical mode decomposition based fringe-like pattern suppression in polarization interference imaging spectrometer

NASA Astrophysics Data System (ADS)

Ren, Wenyi; Cao, Qizhi; Wu, Dan; Jiang, Jiangang; Yang, Guoan; Xie, Yingge; Wang, Guodong; Zhang, Sheqi

2018-01-01

Many observers using interference imaging spectrometer were plagued by the fringe-like pattern(FP) that occurs for optical wavelengths in red and near-infrared region. It brings us more difficulties in the data processing such as the spectrum calibration, information retrieval, and so on. An adaptive method based on the bi-dimensional empirical mode decomposition was developed to suppress the nonlinear FP in polarization interference imaging spectrometer. The FP and corrected interferogram were separated effectively. Meanwhile, the stripes introduced by CCD mosaic was suppressed. The nonlinear interferogram background removal and the spectrum distortion correction were implemented as well. It provides us an alternative method to adaptively suppress the nonlinear FP without prior experimental data and knowledge. This approach potentially is a powerful tool in the fields of Fourier transform spectroscopy, holographic imaging, optical measurement based on moire fringe, etc.

Analyzing Tropical Waves Using the Parallel Ensemble Empirical Model Decomposition Method: Preliminary Results from Hurricane Sandy

NASA Technical Reports Server (NTRS)

Shen, Bo-Wen; Cheung, Samson; Li, Jui-Lin F.; Wu, Yu-ling

2013-01-01

In this study, we discuss the performance of the parallel ensemble empirical mode decomposition (EMD) in the analysis of tropical waves that are associated with tropical cyclone (TC) formation. To efficiently analyze high-resolution, global, multiple-dimensional data sets, we first implement multilevel parallelism into the ensemble EMD (EEMD) and obtain a parallel speedup of 720 using 200 eight-core processors. We then apply the parallel EEMD (PEEMD) to extract the intrinsic mode functions (IMFs) from preselected data sets that represent (1) idealized tropical waves and (2) large-scale environmental flows associated with Hurricane Sandy (2012). Results indicate that the PEEMD is efficient and effective in revealing the major wave characteristics of the data, such as wavelengths and periods, by sifting out the dominant (wave) components. This approach has a potential for hurricane climate study by examining the statistical relationship between tropical waves and TC formation.

Estimation of pulse rate from ambulatory PPG using ensemble empirical mode decomposition and adaptive thresholding.

PubMed

Pittara, Melpo; Theocharides, Theocharis; Orphanidou, Christina

2017-07-01

A new method for deriving pulse rate from PPG obtained from ambulatory patients is presented. The method employs Ensemble Empirical Mode Decomposition to identify the pulsatile component from noise-corrupted PPG, and then uses a set of physiologically-relevant rules followed by adaptive thresholding, in order to estimate the pulse rate in the presence of noise. The method was optimized and validated using 63 hours of data obtained from ambulatory hospital patients. The F1 score obtained with respect to expertly annotated data was 0.857 and the mean absolute errors of estimated pulse rates with respect to heart rates obtained from ECG collected in parallel were 1.72 bpm for "good" quality PPG and 4.49 bpm for "bad" quality PPG. Both errors are within the clinically acceptable margin-of-error for pulse rate/heart rate measurements, showing the promise of the proposed approach for inclusion in next generation wearable sensors.

Gyroscope-driven mouse pointer with an EMOTIV® EEG headset and data analysis based on Empirical Mode Decomposition.

PubMed

Rosas-Cholula, Gerardo; Ramirez-Cortes, Juan Manuel; Alarcon-Aquino, Vicente; Gomez-Gil, Pilar; Rangel-Magdaleno, Jose de Jesus; Reyes-Garcia, Carlos

2013-08-14

This paper presents a project on the development of a cursor control emulating the typical operations of a computer-mouse, using gyroscope and eye-blinking electromyographic signals which are obtained through a commercial 16-electrode wireless headset, recently released by Emotiv. The cursor position is controlled using information from a gyroscope included in the headset. The clicks are generated through the user's blinking with an adequate detection procedure based on the spectral-like technique called Empirical Mode Decomposition (EMD). EMD is proposed as a simple and quick computational tool, yet effective, aimed to artifact reduction from head movements as well as a method to detect blinking signals for mouse control. Kalman filter is used as state estimator for mouse position control and jitter removal. The detection rate obtained in average was 94.9%. Experimental setup and some obtained results are presented.

Gyroscope-Driven Mouse Pointer with an EMOTIV® EEG Headset and Data Analysis Based on Empirical Mode Decomposition

PubMed Central

Rosas-Cholula, Gerardo; Ramirez-Cortes, Juan Manuel; Alarcon-Aquino, Vicente; Gomez-Gil, Pilar; Rangel-Magdaleno, Jose de Jesus; Reyes-Garcia, Carlos

2013-01-01

This paper presents a project on the development of a cursor control emulating the typical operations of a computer-mouse, using gyroscope and eye-blinking electromyographic signals which are obtained through a commercial 16-electrode wireless headset, recently released by Emotiv. The cursor position is controlled using information from a gyroscope included in the headset. The clicks are generated through the user's blinking with an adequate detection procedure based on the spectral-like technique called Empirical Mode Decomposition (EMD). EMD is proposed as a simple and quick computational tool, yet effective, aimed to artifact reduction from head movements as well as a method to detect blinking signals for mouse control. Kalman filter is used as state estimator for mouse position control and jitter removal. The detection rate obtained in average was 94.9%. Experimental setup and some obtained results are presented. PMID:23948873

Adaptive-projection intrinsically transformed multivariate empirical mode decomposition in cooperative brain-computer interface applications.

PubMed

Hemakom, Apit; Goverdovsky, Valentin; Looney, David; Mandic, Danilo P

2016-04-13

An extension to multivariate empirical mode decomposition (MEMD), termed adaptive-projection intrinsically transformed MEMD (APIT-MEMD), is proposed to cater for power imbalances and inter-channel correlations in real-world multichannel data. It is shown that the APIT-MEMD exhibits similar or better performance than MEMD for a large number of projection vectors, whereas it outperforms MEMD for the critical case of a small number of projection vectors within the sifting algorithm. We also employ the noise-assisted APIT-MEMD within our proposed intrinsic multiscale analysis framework and illustrate the advantages of such an approach in notoriously noise-dominated cooperative brain-computer interface (BCI) based on the steady-state visual evoked potentials and the P300 responses. Finally, we show that for a joint cognitive BCI task, the proposed intrinsic multiscale analysis framework improves system performance in terms of the information transfer rate. © 2016 The Author(s).

Towards estimation of respiratory muscle effort with respiratory inductance plethysmography signals and complementary ensemble empirical mode decomposition.

PubMed

Chen, Ya-Chen; Hsiao, Tzu-Chien

2018-07-01

Respiratory inductance plethysmography (RIP) sensor is an inexpensive, non-invasive, easy-to-use transducer for collecting respiratory movement data. Studies have reported that the RIP signal's amplitude and frequency can be used to discriminate respiratory diseases. However, with the conventional approach of RIP data analysis, respiratory muscle effort cannot be estimated. In this paper, the estimation of the respiratory muscle effort through RIP signal was proposed. A complementary ensemble empirical mode decomposition method was used, to extract hidden signals from the RIP signals based on the frequency bands of the activities of different respiratory muscles. To validate the proposed method, an experiment to collect subjects' RIP signal under thoracic breathing (TB) and abdominal breathing (AB) was conducted. The experimental results for both the TB and AB indicate that the proposed method can be used to loosely estimate the activities of thoracic muscles, abdominal muscles, and diaphragm. Graphical abstract ᅟ.

Covariability of Central America/Mexico winter precipitation and tropical sea surface temperatures

NASA Astrophysics Data System (ADS)

Pan, Yutong; Zeng, Ning; Mariotti, Annarita; Wang, Hui; Kumar, Arun; Sánchez, René Lobato; Jha, Bhaskar

2018-06-01

In this study, the relationships between Central America/Mexico (CAM) winter precipitation and tropical Pacific/Atlantic sea surface temperatures (SSTs) are examined based on 68-year (1948-2015) observations and 59-year (1957-2015) atmospheric model simulations forced by observed SSTs. The covariability of the winter precipitation and SSTs is quantified using the singular value decomposition (SVD) method with observational data. The first SVD mode relates out-of-phase precipitation anomalies in northern Mexico and Central America to the tropical Pacific El Niño/La Niña SST variation. The second mode links a decreasing trend in the precipitation over Central America to the warming of SSTs in the tropical Atlantic, as well as in the tropical western Pacific and the tropical Indian Ocean. The first mode represents 67% of the covariance between the two fields, indicating a strong association between CAM winter precipitation and El Niño/La Niña, whereas the second mode represents 20% of the covariance. The two modes account for 32% of CAM winter precipitation variance, of which, 17% is related to the El Niño/La Niña SST and 15% is related to the SST warming trend. The atmospheric circulation patterns, including 500-hPa height and low-level winds obtained by linear regressions against the SVD SST time series, are dynamically consistent with the precipitation anomaly patterns. The model simulations driven by the observed SSTs suggest that these precipitation anomalies are likely a response to tropical SST forcing. It is also shown that there is significant potential predictability of CAM winter precipitation given tropical SST information.

Simulation of Decomposition Kinetics of Supercooled Austenite in Powder Steel

NASA Astrophysics Data System (ADS)

Tsyganova, M. S.; Ivashko, A. G.; Polyshuk, I. N.; Nabatov, R. I.; Tsyganova, A. I.

2017-10-01

To approve heat treatment of steel modes, quantitative data on austenite decomposition are required. Gaining these data experimentally appears to be extremely complicated. In present work, few approaches to simulate the phase transformation process are proposed considering structure characteristics of powder steels. Results of comparative analysis of these approaches are also given. Predicting the transformation kinetics by simulation is verified for PK40N2M (0.38% C, 2.10% Ni, 0.40% Mo) steel with 3% porosity and PK80 (0.80% C) steel with different porosity using published experimental data.

A New View of Earthquake Ground Motion Data: The Hilbert Spectral Analysis

NASA Technical Reports Server (NTRS)

Huang, Norden; Busalacchi, Antonio J. (Technical Monitor)

2000-01-01

A brief description of the newly developed Empirical Mode Decomposition (ENID) and Hilbert Spectral Analysis (HSA) method will be given. The decomposition is adaptive and can be applied to both nonlinear and nonstationary data. Example of the method applied to a sample earthquake record will be given. The results indicate those low frequency components, totally missed by the Fourier analysis, are clearly identified by the new method. Comparisons with Wavelet and window Fourier analysis show the new method offers much better temporal and frequency resolutions.

Output-only modal dynamic identification of frames by a refined FDD algorithm at seismic input and high damping

NASA Astrophysics Data System (ADS)

Pioldi, Fabio; Ferrari, Rosalba; Rizzi, Egidio

2016-02-01

The present paper deals with the seismic modal dynamic identification of frame structures by a refined Frequency Domain Decomposition (rFDD) algorithm, autonomously formulated and implemented within MATLAB. First, the output-only identification technique is outlined analytically and then employed to characterize all modal properties. Synthetic response signals generated prior to the dynamic identification are adopted as input channels, in view of assessing a necessary condition for the procedure's efficiency. Initially, the algorithm is verified on canonical input from random excitation. Then, modal identification has been attempted successfully at given seismic input, taken as base excitation, including both strong motion data and single and multiple input ground motions. Rather than different attempts investigating the role of seismic response signals in the Time Domain, this paper considers the identification analysis in the Frequency Domain. Results turn-out very much consistent with the target values, with quite limited errors in the modal estimates, including for the damping ratios, ranging from values in the order of 1% to 10%. Either seismic excitation and high values of damping, resulting critical also in case of well-spaced modes, shall not fulfill traditional FFD assumptions: this shows the consistency of the developed algorithm. Through original strategies and arrangements, the paper shows that a comprehensive rFDD modal dynamic identification of frames at seismic input is feasible, also at concomitant high damping.

Forest composition modifies litter dynamics and decomposition in regenerating tropical dry forest.

PubMed

Schilling, Erik M; Waring, Bonnie G; Schilling, Jonathan S; Powers, Jennifer S

2016-09-01

We investigated how forest composition, litter quality, and rainfall interact to affect leaf litter decomposition across three successional tropical dry forests in Costa Rica. We monitored litter stocks and bulk litter turnover in 18 plots that exhibit substantial variation in soil characteristics, tree community structure, fungal communities (including forests dominated by ecto- or arbuscular mycorrhizal host trees), and forest age. Simultaneously, we decomposed three standard litter substrates over a 6-month period spanning an unusually intense drought. Decay rates of standard substrates depended on the interaction between litter identity and forest type. Decomposition rates were correlated with tree and soil fungal community composition as well as soil fertility, but these relationships differed among litter types. In low fertility soils dominated by ectomycorrhizal oak trees, bulk litter turnover rates were low, regardless of soil moisture. By contrast, in higher fertility soils that supported mostly arbuscular mycorrhizal trees, bulk litter decay rates were strongly dependent on seasonal water availability. Both measures of decomposition increased with forest age, as did the frequency of termite-mediated wood decay. Taken together, our results demonstrate that soils and forest age exert strong control over decomposition dynamics in these tropical dry forests, either directly through effects on microclimate and nutrients, or indirectly by affecting tree and microbial community composition and traits, such as litter quality.

Function of bacterial community dynamics in the formation of cadaveric semiochemicals during in situ carcass decomposition.

PubMed

Pascual, Javier; von Hoermann, Christian; Rottler-Hoermann, Ann-Marie; Nevo, Omer; Geppert, Alicia; Sikorski, Johannes; Huber, Katharina J; Steiger, Sandra; Ayasse, Manfred; Overmann, Jörg

2017-08-01

The decomposition of dead mammalian tissue involves a complex temporal succession of epinecrotic bacteria. Microbial activity may release different cadaveric volatile organic compounds which in turn attract other key players of carcass decomposition such as scavenger insects. To elucidate the dynamics and potential functions of epinecrotic bacteria on carcasses, we monitored bacterial communities developing on still-born piglets incubated in different forest ecosystems by combining high-throughput Illumina 16S rRNA sequencing with gas chromatography-mass spectrometry of volatiles. Our results show that the community structure of epinecrotic bacteria and the types of cadaveric volatile compounds released over the time course of decomposition are driven by deterministic rather than stochastic processes. Individual cadaveric volatile organic compounds were correlated with specific taxa during the first stages of decomposition which are dominated by bacteria. Through best-fitting multiple linear regression models, the synthesis of acetic acid, indole and phenol could be linked to the activity of Enterobacteriaceae, Tissierellaceae and Xanthomonadaceae, respectively. These conclusions are also commensurate with the metabolism described for the dominant taxa identified for these families. The predictable nature of in situ synthesis of cadaveric volatile organic compounds by epinecrotic bacteria provides a new basis for future chemical ecology and forensic studies. © 2017 Society for Applied Microbiology and John Wiley & Sons Ltd.

Collaborative Research: Process-resolving Decomposition of the Global Temperature Response to Modes of Low Frequency Variability in a Changing Climate

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cai, Ming; Deng, Yi

2015-02-06

El Niño-Southern Oscillation (ENSO) and Annular Modes (AMs) represent respectively the most important modes of low frequency variability in the tropical and extratropical circulations. The future projection of the ENSO and AM variability, however, remains highly uncertain with the state-of-the-art coupled general circulation models. A comprehensive understanding of the factors responsible for the inter-model discrepancies in projecting future changes in the ENSO and AM variability, in terms of multiple feedback processes involved, has yet to be achieved. The proposed research aims to identify sources of such uncertainty and establish a set of process-resolving quantitative evaluations of the existing predictions ofmore » the future ENSO and AM variability. The proposed process-resolving evaluations are based on a feedback analysis method formulated in Lu and Cai (2009), which is capable of partitioning 3D temperature anomalies/perturbations into components linked to 1) radiation-related thermodynamic processes such as cloud and water vapor feedbacks, 2) local dynamical processes including convection and turbulent/diffusive energy transfer and 3) non-local dynamical processes such as the horizontal energy transport in the oceans and atmosphere. Taking advantage of the high-resolution, multi-model ensemble products from the Coupled Model Intercomparison Project Phase 5 (CMIP5) soon to be available at the Lawrence Livermore National Lab, we will conduct a process-resolving decomposition of the global three-dimensional (3D) temperature (including SST) response to the ENSO and AM variability in the preindustrial, historical and future climate simulated by these models. Specific research tasks include 1) identifying the model-observation discrepancies in the global temperature response to ENSO and AM variability and attributing such discrepancies to specific feedback processes, 2) delineating the influence of anthropogenic radiative forcing on the key feedback processes operating on ENSO and AM variability and quantifying their relative contributions to the changes in the temperature anomalies associated with different phases of ENSO and AMs, and 3) investigating the linkages between model feedback processes that lead to inter-model differences in time-mean temperature projection and model feedback processes that cause inter-model differences in the simulated ENSO and AM temperature response. Through a thorough model-observation and inter-model comparison of the multiple energetic processes associated with ENSO and AM variability, the proposed research serves to identify key uncertainties in model representation of ENSO and AM variability, and investigate how the model uncertainty in predicting time-mean response is related to the uncertainty in predicting response of the low-frequency modes. The proposal is thus a direct response to the first topical area of the solicitation: Interaction of Climate Change and Low Frequency Modes of Natural Climate Variability. It ultimately supports the accomplishment of the BER climate science activity Long Term Measure (LTM): "Deliver improved scientific data and models about the potential response of the Earth's climate and terrestrial biosphere to increased greenhouse gas levels for policy makers to determine safe levels of greenhouse gases in the atmosphere."« less

A global perspective of the limits of prediction skill based on the ECMWF ensemble

NASA Astrophysics Data System (ADS)

Zagar, Nedjeljka

2016-04-01

In this talk presents a new model of the global forecast error growth applied to the forecast errors simulated by the ensemble prediction system (ENS) of the ECMWF. The proxy for forecast errors is the total spread of the ECMWF operational ensemble forecasts obtained by the decomposition of the wind and geopotential fields in the normal-mode functions. In this way, the ensemble spread can be quantified separately for the balanced and inertio-gravity (IG) modes for every forecast range. Ensemble reliability is defined for the balanced and IG modes comparing the ensemble spread with the control analysis in each scale. The results show that initial uncertainties in the ECMWF ENS are largest in the tropical large-scale modes and their spatial distribution is similar to the distribution of the short-range forecast errors. Initially the ensemble spread grows most in the smallest scales and in the synoptic range of the IG modes but the overall growth is dominated by the increase of spread in balanced modes in synoptic and planetary scales in the midlatitudes. During the forecasts, the distribution of spread in the balanced and IG modes grows towards the climatological spread distribution characteristic of the analyses. The ENS system is found to be somewhat under-dispersive which is associated with the lack of tropical variability, primarily the Kelvin waves. The new model of the forecast error growth has three fitting parameters to parameterize the initial fast growth and a more slow exponential error growth later on. The asymptotic values of forecast errors are independent of the exponential growth rate. It is found that the asymptotic values of the errors due to unbalanced dynamics are around 10 days while the balanced and total errors saturate in 3 to 4 weeks. Reference: Žagar, N., R. Buizza, and J. Tribbia, 2015: A three-dimensional multivariate modal analysis of atmospheric predictability with application to the ECMWF ensemble. J. Atmos. Sci., 72, 4423-4444.

Does cattle grazing of dual-purpose wheat accelerate the rate of stubble decomposition and nutrients released

USDA-ARS?s Scientific Manuscript database

Decomposition and nutrient release of winter annual forages in integrated crop-livestock systems could be affected by the resultant alterations in structure and quality of residues caused by grazing, but little information is available to test this hypothesis. Information on residue dynamics is need...

Importance of vegetation dynamics for future terrestrial carbon cycling

NASA Astrophysics Data System (ADS)

Ahlström, Anders; Xia, Jianyang; Arneth, Almut; Luo, Yiqi; Smith, Benjamin

2015-05-01

Terrestrial ecosystems currently sequester about one third of anthropogenic CO2 emissions each year, an important ecosystem service that dampens climate change. The future fate of this net uptake of CO2 by land based ecosystems is highly uncertain. Most ecosystem models used to predict the future terrestrial carbon cycle share a common architecture, whereby carbon that enters the system as net primary production (NPP) is distributed to plant compartments, transferred to litter and soil through vegetation turnover and then re-emitted to the atmosphere in conjunction with soil decomposition. However, while all models represent the processes of NPP and soil decomposition, they vary greatly in their representations of vegetation turnover and the associated processes governing mortality, disturbance and biome shifts. Here we used a detailed second generation dynamic global vegetation model with advanced representation of vegetation growth and mortality, and the associated turnover. We apply an emulator that describes the carbon flows and pools exactly as in simulations with the full model. The emulator simulates ecosystem dynamics in response to 13 different climate or Earth system model simulations from the Coupled Model Intercomparison Project Phase 5 ensemble under RCP8.5 radiative forcing. By exchanging carbon cycle processes between these 13 simulations we quantified the relative roles of three main driving processes of the carbon cycle; (I) NPP, (II) vegetation dynamics and turnover and (III) soil decomposition, in terms of their contribution to future carbon (C) uptake uncertainties among the ensemble of climate change scenarios. We found that NPP, vegetation turnover (including structural shifts, wild fires and mortality) and soil decomposition rates explained 49%, 17% and 33%, respectively, of uncertainties in modelled global C-uptake. Uncertainty due to vegetation turnover was further partitioned into stand-clearing disturbances (16%), wild fires (0%), stand dynamics (7%), reproduction (10%) and biome shifts (67%) globally. We conclude that while NPP and soil decomposition rates jointly account for 83% of future climate induced C-uptake uncertainties, vegetation turnover and structure, dominated by biome shifts, represent a significant fraction globally and regionally (tropical forests: 40%), strongly motivating their representation and analysis in future C-cycle studies.

Ensemble Empirical Mode Decomposition based methodology for ultrasonic testing of coarse grain austenitic stainless steels.

PubMed

Sharma, Govind K; Kumar, Anish; Jayakumar, T; Purnachandra Rao, B; Mariyappa, N

2015-03-01

A signal processing methodology is proposed in this paper for effective reconstruction of ultrasonic signals in coarse grained high scattering austenitic stainless steel. The proposed methodology is comprised of the Ensemble Empirical Mode Decomposition (EEMD) processing of ultrasonic signals and application of signal minimisation algorithm on selected Intrinsic Mode Functions (IMFs) obtained by EEMD. The methodology is applied to ultrasonic signals obtained from austenitic stainless steel specimens of different grain size, with and without defects. The influence of probe frequency and data length of a signal on EEMD decomposition is also investigated. For a particular sampling rate and probe frequency, the same range of IMFs can be used to reconstruct the ultrasonic signal, irrespective of the grain size in the range of 30-210 μm investigated in this study. This methodology is successfully employed for detection of defects in a 50mm thick coarse grain austenitic stainless steel specimens. Signal to noise ratio improvement of better than 15 dB is observed for the ultrasonic signal obtained from a 25 mm deep flat bottom hole in 200 μm grain size specimen. For ultrasonic signals obtained from defects at different depths, a minimum of 7 dB extra enhancement in SNR is achieved as compared to the sum of selected IMF approach. The application of minimisation algorithm with EEMD processed signal in the proposed methodology proves to be effective for adaptive signal reconstruction with improved signal to noise ratio. This methodology was further employed for successful imaging of defects in a B-scan. Copyright © 2014. Published by Elsevier B.V.

Fast multidimensional ensemble empirical mode decomposition for the analysis of big spatio-temporal datasets.

PubMed

Wu, Zhaohua; Feng, Jiaxin; Qiao, Fangli; Tan, Zhe-Min

2016-04-13

In this big data era, it is more urgent than ever to solve two major issues: (i) fast data transmission methods that can facilitate access to data from non-local sources and (ii) fast and efficient data analysis methods that can reveal the key information from the available data for particular purposes. Although approaches in different fields to address these two questions may differ significantly, the common part must involve data compression techniques and a fast algorithm. This paper introduces the recently developed adaptive and spatio-temporally local analysis method, namely the fast multidimensional ensemble empirical mode decomposition (MEEMD), for the analysis of a large spatio-temporal dataset. The original MEEMD uses ensemble empirical mode decomposition to decompose time series at each spatial grid and then pieces together the temporal-spatial evolution of climate variability and change on naturally separated timescales, which is computationally expensive. By taking advantage of the high efficiency of the expression using principal component analysis/empirical orthogonal function analysis for spatio-temporally coherent data, we design a lossy compression method for climate data to facilitate its non-local transmission. We also explain the basic principles behind the fast MEEMD through decomposing principal components instead of original grid-wise time series to speed up computation of MEEMD. Using a typical climate dataset as an example, we demonstrate that our newly designed methods can (i) compress data with a compression rate of one to two orders; and (ii) speed-up the MEEMD algorithm by one to two orders. © 2016 The Authors.

Analysis of Factors Influencing PM2.5 in Beijing: A Microcosmic and Dynamic Perspective for Sustainable Development

NASA Astrophysics Data System (ADS)

Wang, Yani; Wang, Jun; Tao, Guiping

2017-12-01

Haze pollution has become a hot issue concerned with the process of modernization and one serious problem requiring urgent solution, especially in Beijing. PM2.5 is the main reason causing haze and its harm. Although there has been research centering on factors affecting PM2.5, little attention has been devoted to the microcosmic and dynamic effects on it. Vector auto-regression (VAR) mode is applied in this study to explore the interaction between PM2.5, PM10, SO2, CO and NO2. Results of Granger causality tests tell that there exists causal relationship between PM10, SO2, CO, NO2 and PM2.5. Impulse response functions (IRFs) show that the response of PM2.5 to a shock in CO is positive and large in the short period, while the reaction of PM2.5 to a shock in SO2 increases over time. Meanwhile, variance decomposition indicate that PM2.5 is more closely related to CO in the short term while SO2’ influence accounts for a higher proportion in the long run. The findings provide a novel perspective to analyze the factors influencing PM2.5 dynamically and contribute to a better understanding of haze and its relationship with sustainable development.